Intro to wind energy

Exercise 1

Map wind turbines in Rogaland from OSM and calculate the total energy capacity installed.

Objetives

  • OSM (and how get data from it)
  • Map points data (interactive map)
  • Manipulate spatial data (e.g., intersection points - polygons)
  • Types of data in a data frame
  • Data wrangling

Solution

Load libraries

Show the code
library(osmdata)
library(sf)
library(tmap)
library(tidyverse)

Get data from OpenStreetMap with osmdata, and transform to a sf object.

Show the code
query <- opq(bbox = "Rogaland", timeout = 50) |> 
   add_osm_feature(key = "generator:source", value = "wind")
data <-  osmdata_sf(query)

wind_turbines <- data$osm_points 

Names of the data frame,

Show the code
wind_turbines |>
  names()
 [1] "osm_id"                           "name"                            
 [3] "aeroway:light"                    "aeroway:light:character"         
 [5] "aeroway:light:colour"             "aeroway:light:icao_type"         
 [7] "aeroway:light:intensity"          "brand"                           
 [9] "date_constructed"                 "description"                     
[11] "ele"                              "generator:method"                
[13] "generator:output:electricity"     "generator:source"                
[15] "generator:type"                   "height"                          
[17] "man_made"                         "model"                           
[19] "operator"                         "power"                           
[21] "ref"                              "ref:hinder"                      
[23] "ref:kystverket"                   "seamark:landmark:category"       
[25] "seamark:landmark:conspicuity"     "seamark:landmark:vertical_length"
[27] "seamark:light:1:character"        "seamark:light:1:colour"          
[29] "seamark:light:1:group"            "seamark:light:1:height"          
[31] "seamark:light:1:multiple"         "seamark:light:1:period"          
[33] "seamark:light:1:range"            "seamark:light:2:category"        
[35] "seamark:light:2:character"        "seamark:light:2:colour"          
[37] "seamark:name"                     "seamark:type"                    
[39] "serial_number"                    "source"                          
[41] "geometry"                        

Clean names

Show the code
wind_turbines <- wind_turbines |>
  janitor::clean_names()

names(wind_turbines)
 [1] "osm_id"                           "name"                            
 [3] "aeroway_light"                    "aeroway_light_character"         
 [5] "aeroway_light_colour"             "aeroway_light_icao_type"         
 [7] "aeroway_light_intensity"          "brand"                           
 [9] "date_constructed"                 "description"                     
[11] "ele"                              "generator_method"                
[13] "generator_output_electricity"     "generator_source"                
[15] "generator_type"                   "height"                          
[17] "man_made"                         "model"                           
[19] "operator"                         "power"                           
[21] "ref"                              "ref_hinder"                      
[23] "ref_kystverket"                   "seamark_landmark_category"       
[25] "seamark_landmark_conspicuity"     "seamark_landmark_vertical_length"
[27] "seamark_light_1_character"        "seamark_light_1_colour"          
[29] "seamark_light_1_group"            "seamark_light_1_height"          
[31] "seamark_light_1_multiple"         "seamark_light_1_period"          
[33] "seamark_light_1_range"            "seamark_light_2_category"        
[35] "seamark_light_2_character"        "seamark_light_2_colour"          
[37] "seamark_name"                     "seamark_type"                    
[39] "serial_number"                    "source"                          
[41] "geometry"                        

Interactive map with tmap.

Show the code
tmap_mode("view")

tm_shape(wind_turbines) +
  tm_dots(col = "#0072B2")
Figure 1: Wind turbines in Rogaland (Data from OSM)

There are some wind turbines that ar not in Rogaland, so we are going to delect them from the dataset. For that we need to intersect our points (wind turbines) with the polygon (Rogaland).

Show the code
# Get Norwegian counties (polygons)) from GISCO
counties <- giscoR::gisco_get_nuts(country = "NO",
                                   year = "2021",
                                   nuts_level = 3,
                                   epsg = "4326",
                                   resolution = "01") 
# Get only rogaland county
rogaland <- counties |> 
  filter(NUTS_NAME == "Rogaland")

# Intersect wind turbines (points) with rogaland (polygons)
wind_turbines <- wind_turbines |> 
  st_intersection(rogaland)

# Plot
tm_shape(wind_turbines) +
  tm_dots(col = "#0072B2")
Figure 2: Wind turbines in Rogaland (Data from OSM)

Now, we are going to calculate the maximum capacity (MW) installed in the region. If we inspect the data, there is a column describing called generator:output:electricity, which give the information we are looking for in MW. However, if we inspect the columns we see that the values are characters (<chr>) not numbers, so we can not carried out numerical operations on them.

Show the code
glimpse(wind_turbines)
Rows: 256
Columns: 50
$ osm_id                           <chr> "1277731149", "1510248163", "15102482…
$ name                             <chr> "Høg-Jæren energipark 31", "Høg-Jæren…
$ aeroway_light                    <chr> "obstacle", NA, "obstacle", "obstacle…
$ aeroway_light_character          <chr> "flashing", NA, "flashing", "flashing…
$ aeroway_light_colour             <chr> "red", NA, "red", "red", "red", "red"…
$ aeroway_light_icao_type          <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ aeroway_light_intensity          <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ brand                            <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ date_constructed                 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ description                      <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ele                              <chr> "264", "268", "250", "234", "205", "2…
$ generator_method                 <chr> "wind_turbine", "wind_turbine", "wind…
$ generator_output_electricity     <chr> "2.3 MW", "2.3 MW", "2.3 MW", "2.3 MW…
$ generator_source                 <chr> "wind", "wind", "wind", "wind", "wind…
$ generator_type                   <chr> "horizontal_axis", "horizontal_axis",…
$ height                           <chr> "126", "126", "126", "126", "89", "89…
$ man_made                         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ model                            <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ operator                         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ power                            <chr> "generator", "generator", "generator"…
$ ref                              <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ ref_hinder                       <chr> "70647", "70646", "70640", "71472", "…
$ ref_kystverket                   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_landmark_category        <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_landmark_conspicuity     <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_landmark_vertical_length <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_1_character        <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_1_colour           <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_1_group            <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_1_height           <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_1_multiple         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_1_period           <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_1_range            <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_2_category         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_2_character        <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_light_2_colour           <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_name                     <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ seamark_type                     <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ serial_number                    <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ source                           <chr> "Kartverket Luftfartshindre", "Kartve…
$ NUTS_ID                          <chr> "NO0A1", "NO0A1", "NO0A1", "NO0A1", "…
$ LEVL_CODE                        <dbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3…
$ URBN_TYPE                        <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ CNTR_CODE                        <chr> "NO", "NO", "NO", "NO", "NO", "NO", "…
$ NAME_LATN                        <chr> "Rogaland", "Rogaland", "Rogaland", "…
$ NUTS_NAME                        <chr> "Rogaland", "Rogaland", "Rogaland", "…
$ MOUNT_TYPE                       <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ COAST_TYPE                       <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ geo                              <chr> "NO0A1", "NO0A1", "NO0A1", "NO0A1", "…
$ geometry                         <POINT [°]> POINT (5.792002 58.65093), POIN…

Therefore, we need to transform the data to numbers. We can use the function parse_number() from the package readr (inside tidyverse). For not deleting the column, we can generate a new one (e.g., gen_electricity_mw)

Show the code
wind_turbines <- wind_turbines |> 
  mutate(gen_electricity_mw = parse_number(generator_output_electricity) )
     
# Show first 10 values of the column
wind_turbines$gen_electricity_mw |> 
  head(10)
 [1] 2.3 2.3 2.3 2.3  NA  NA 2.3 2.3 2.3 2.3

Remove NA in energy generator:

Show the code
wind_turbines <- wind_turbines |> 
  drop_na(gen_electricity_mw)

# Summary of energy generator (without NA) 
wind_turbines$gen_electricity_mw |> 
  head(10)
 [1] 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3

calculate the total generation energy capacity.

Show the code
total_capacity_mw <- wind_turbines$gen_electricity_mw |> sum()

print(total_capacity_mw)
[1] 905.3

So the total wind capacity installed in Rogaland is 905.3 MW. Note, that this is only the maximum energy installed and not how much it is generate in the region. It does not take into account the efficiency of the turbines nor wind availability!!.

Exercise 2

Map wind farms and wind turbines from NVE (www.nve.no). The data are free but need to be downloaded from https://nedlasting.nve.no/gis/ before reading into R (save it in a folder: e.g., ~/data/big_data/NVE/NVEData). I have downloaded them in .geojson format. Therefore, we need to read them with the geojsonsf package, which converts GeoJSON to sf objects.

Objetives

  • Load data from local files
  • Data wrangling (preprocessing)
  • Formats of spatial data
  • Spatial intersections (i.e., points - polygons)
  • Types of vector data (i.e., lines, points, polygons)
  • Plot more that one layer in a interactive map

Solution

Show the code
# Libraries
library(geojsonsf)
library(sf)
library(tidyverse)
library(tmap)

Load data from a local file.

  1. Wind turbines (point data)
Show the code
wind_turbines_nve_path <- "data/big_data/NVE/NVEData/Vindkraft_Vindturbin.geojson"
wind_turbines_nve <- geojson_sf(wind_turbines_nve_path) |> 
  # dataUttaksdato to date format
  mutate(dataUttaksdato = ymd(dataUttaksdato))

wind_turbines_nve
Simple feature collection with 272 features and 5 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 4.905257 ymin: 58.31454 xmax: 6.51592 ymax: 59.41634
Geodetic CRS:  WGS 84
First 10 features:
   objektType   saksTittel saksKategori dataUttaksdato              eksportType
1  Vindturbin      Svåheia            2     2024-02-07 NVEs nedlastningsløsning
2  Vindturbin       Tysvær            2     2024-02-07 NVEs nedlastningsløsning
3  Vindturbin    Høg-Jæren            2     2024-02-07 NVEs nedlastningsløsning
4  Vindturbin       Tysvær            2     2024-02-07 NVEs nedlastningsløsning
5  Vindturbin    Bjerkreim            2     2024-02-07 NVEs nedlastningsløsning
6  Vindturbin Vardafjellet            2     2024-02-07 NVEs nedlastningsløsning
7  Vindturbin    Bjerkreim            2     2024-02-07 NVEs nedlastningsløsning
8  Vindturbin   Måkaknuten            2     2024-02-07 NVEs nedlastningsløsning
9  Vindturbin       Utsira            2     2024-02-07 NVEs nedlastningsløsning
10 Vindturbin     Tellenes            2     2024-02-07 NVEs nedlastningsløsning
                    geometry
1  POINT (6.091838 58.38779)
2  POINT (5.552303 59.31521)
3   POINT (5.76918 58.64514)
4   POINT (5.557336 59.2931)
5  POINT (5.934883 58.59081)
6  POINT (5.894319 58.83959)
7  POINT (5.945595 58.58838)
8  POINT (5.966087 58.68458)
9  POINT (4.906018 59.31497)
10 POINT (6.514851 58.34872)
  1. Wind farms areas (Polygons)
Show the code
wind_farms_nve_path <- "data/big_data/NVE/NVEData/Vindkraft_VindkraftanleggOmr.geojson"
wind_farms_nve <- geojson_sf(wind_farms_nve_path) |> 
  # Coherce to dates format
  mutate(across(.cols = ends_with("dato"), .fns = ymd)) |> 
  # Add ID column
   rowid_to_column("id_farm")

wind_farms_nve
Simple feature collection with 57 features and 15 fields
Geometry type: GEOMETRY
Dimension:     XY
Bounding box:  xmin: 4.890143 ymin: 58.2376 xmax: 6.520988 ymax: 59.61306
Geodetic CRS:  WGS 84
First 10 features:
   id_farm dataUttaksdato totaltAntTurbiner    objektType utAvDriftDato
1        1     2024-02-07              <NA> Vindkraftverk          <NA>
2        2     2024-02-07              <NA> Vindkraftverk          <NA>
3        3     2024-02-07              <NA> Vindkraftverk          <NA>
4        4     2024-02-07              <NA> Vindkraftverk          <NA>
5        5     2024-02-07                37 Vindkraftverk          <NA>
6        6     2024-02-07              <NA> Vindkraftverk          <NA>
7        7     2024-02-07              <NA> Vindkraftverk          <NA>
8        8     2024-02-07              <NA> Vindkraftverk          <NA>
9        9     2024-02-07              <NA> Vindkraftverk          <NA>
10      10     2024-02-07              <NA> Vindkraftverk          <NA>
   idriftDato status fylkesnavn
1        <NA>      V   Rogaland
2        <NA>      V   Rogaland
3        <NA>      V   Rogaland
4        <NA>     FJ   Rogaland
5  2019-12-19      D   Rogaland
6        <NA>     FJ   Rogaland
7        <NA>     FJ   Rogaland
8        <NA>      V   Rogaland
9        <NA>      V   Rogaland
10       <NA>     FJ   Rogaland
                                                                    saksLenke
1  https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=194
2   https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=29
3   https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=36
4   https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=52
5   https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=32
6   https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=37
7   https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=64
8   https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=55
9   https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=53
10 https://www.nve.no/konsesjon/konsesjonssaker/konsesjonssak?type=A-6&id=120
   forventetProduksjon_Gwh kommunenavn                       geometry
1                     34.0   Stavanger POLYGON ((5.616381 59.07174...
2                    408.0   Bjerkreim POLYGON ((5.892451 58.63726...
3                    785.4   Bjerkreim POLYGON ((5.893369 58.70034...
4                    272.0        Lund POLYGON ((6.26167 58.56276,...
5                    558.4   Bjerkreim POLYGON ((5.995785 58.58672...
6                    306.0   Eigersund POLYGON ((6.206857 58.56671...
7                    306.0     Sokndal POLYGON ((6.153781 58.36661...
8                    544.0     Gjesdal POLYGON ((6.293888 58.82249...
9                    265.2   Bjerkreim POLYGON ((6.07609 58.68459,...
10                   204.0      Karmøy POLYGON ((5.29149 59.23401,...
   saksKategori               tiltakshaver effekt_MW              eksportType
1             2 MARIN ENERGI TESTSENTER AS      10.0 NVEs nedlastningsløsning
2             3         LYSE PRODUKSJON AS     120.0 NVEs nedlastningsløsning
3             3         LYSE PRODUKSJON AS     231.0 NVEs nedlastningsløsning
4             4          BJERKREIM VIND AS      80.0 NVEs nedlastningsløsning
5             2          BJERKREIM VIND AS     159.1 NVEs nedlastningsløsning
6             4          BJERKREIM VIND AS      90.0 NVEs nedlastningsløsning
7             4                  ZEPHYR AS      90.0 NVEs nedlastningsløsning
8             2     GILJA VINDKRAFTVERK AS     160.0 NVEs nedlastningsløsning
9             3                  ZEPHYR AS      78.0 NVEs nedlastningsløsning
10            4      EQUINOR WIND POWER AS      60.0 NVEs nedlastningsløsning

Map both datasets together:

Show the code
tmap_mode("view")


tm_shape(wind_farms_nve) + 
  tm_fill("status", alpha = 0.5, title = "Wind farm status") +
  # Add wind turbines
  tm_shape(wind_turbines_nve) +
  tm_dots(col = "#0072B2")
Figure 3: Wind turbines in Rogaland (Data from NVE)

Note: Status

D - Drift (Operations) N - Nedlagt (Decommissioned) O - Ombygd (Rebuilt) P - Planlagt (Planned) P1 - Planlagt illustrert (Planed illustrated) P2 - Planlagt, prosjektert (Planed, projected) U - Under arbeid (in progress) V - Vedtatt (Adopted) FJ - Fjernet (Removed)

To calculate the actual capacity installed, we need to select only the wind farms that are in operation from wind_farms_nve, and sum the power capacity (effekt_MW). We can do that by montds to see the temporal evolution.

Show the code
power_year <- wind_farms_nve |>
  # Get only farm in operation
  filter(status == "D") |> 
  # Summarize power by month
  group_by(year = lubridate::floor_date(idriftDato, "year")) %>%
  summarize(power_MW = sum(effekt_MW)) |> 
  ungroup() |> 
  # Cummulative sum
  mutate(cumsum_power_MW = cumsum(power_MW))

# Column plot
ggplot(data = power_year,
       aes(x = year, y = cumsum_power_MW)) +
  geom_col(fill = "darkblue") +
  labs(title = "Cumulative wind power installed in Rogaland",
       y = "Power [MW]",
       x = "") +
  theme_bw()

Wind capacity

We can count now the number of wind turbines per wind farm, to understand for example the volume of blades we may need to recycler.

Show the code
 # Intersect points (wind turbines) wit polygons (wind farms)
number_turbines_farm <- wind_turbines_nve |> 
  # Detect wind farm 
  st_intersection(wind_farms_nve) |> 
  # Number of turbines 
  group_by(id_farm, status,  idriftDato, effekt_MW) |> 
  summarize(n = n()) |> 
  ungroup()

number_turbines_farm
Simple feature collection with 28 features and 5 fields
Geometry type: GEOMETRY
Dimension:     XY
Bounding box:  xmin: 4.905257 ymin: 58.31454 xmax: 6.51592 ymax: 59.41634
Geodetic CRS:  WGS 84
# A tibble: 28 × 6
   id_farm status idriftDato effekt_MW     n                            geometry
     <int> <chr>  <date>         <dbl> <int>                      <GEOMETRY [°]>
 1       2 V      NA              120      1           POINT (5.885113 58.58739)
 2       3 V      NA              231     22 MULTIPOINT ((5.909491 58.66277), (…
 3       5 D      2019-12-19      159.    37 MULTIPOINT ((5.902099 58.58625), (…
 4      14 D      2019-07-25       90     18 MULTIPOINT ((5.871099 58.57491), (…
 5      16 D      2020-06-22       30      7 MULTIPOINT ((5.955698 58.66794), (…
 6      19 D      2020-08-17       30      7 MULTIPOINT ((5.88813 58.8301), (5.…
 7      29 D      2018-02-13       10      3 MULTIPOINT ((5.93595 58.72669), (5…
 8      30 D      2018-10-12       10      2 MULTIPOINT ((5.908438 58.74937), (…
 9      32 P2     NA               15      4 MULTIPOINT ((4.905952 59.31499), (…
10      33 D      2017-11-16      110     33 MULTIPOINT ((6.119956 58.4561), (6…
# ℹ 18 more rows

Now we are going to plot the evolution but in the number od witd turbines in operation:

Show the code
turbines_year <- number_turbines_farm |>
  # Get only farm in operation
  filter(status == "D") |> 
  # Summarize power by month
  group_by(year = lubridate::floor_date(idriftDato, "year")) %>%
  summarize(num_turbines = sum(n)) |> 
  ungroup() |> 
  # Cummulative sum
  mutate(cumsum_num_turbines = cumsum(num_turbines))

# Column plot
ggplot(data = turbines_year,
       aes(x = year, y = cumsum_num_turbines)) +
  geom_col(fill = "darkblue") +
  labs(title = "Cumulative number of wind turbines installed in Rogaland",
       y = "",
       x = "") +
  theme_bw()

How would you improve these figures? For example, we can change the background colour, add subtitles and captions, change font sizes, etc. Can you generate your own figure? Why do you think it looks better? As an example I have created this one, what do you think?

Show the code
caption_text <- "Data source: The Norwegian Water Resources and Energy Directorate (NVE)\nAuthor: Javier Elío (@Elio_javi) - Western Norway University of Applied Sciences"

# Column plot
ggplot(data = power_year,
       aes(x = year, y = cumsum_power_MW)) +
  geom_col(fill = "#0072B2") +
  labs(title = "Wind energy in Rogaland (Norway)",
       subtitle = "Cumulative power installed capacity in MW",
       caption = caption_text,
       y = "",
       x = "") +
  expand_limits(y = c(0, 1700)) +
  theme_bw() +
  theme(
    # Title and captions
    plot.title = element_text(size = 15, colour = "darkblue", face = "bold"),
    plot.subtitle = element_text(size = 10, colour = "grey25"),
    plot.caption = element_text(size = 10, colour = "grey25"),
    # Background colour
    plot.background = element_rect(fill = "linen", colour = NA),
    panel.background = element_rect(fill = "grey85", colour = NA)
  ) +
  # Add arrow
  annotate(
    'curve',
    x = as.Date("2016-01-01"), # Play around with the coordinates until you're satisfied
    y = 800,
    yend = 1600,
    xend = as.Date("2021-01-01"),
    linewidth = 1.5,
    curvature = 0.3,
    col = "#D55E00",
    arrow = arrow(length = unit(0.5, 'cm'))
  ) +
  # Add text
  annotate(
    'text',
    x = as.Date("2012-06-01"),
    y = 1300,
    label = "The installed capacity has nearly\ndoubled between 2017 and 2021.\nWhat will the limit be?",
    size = 3.5,
    hjust = 0
  )

Exercise 3

Objetives

  • Get wind data from NORA3 link.

The R-script is based on matlab fucntions link

Solution

Show the code
library(terra)
library(tidyterra)
library(eurostat)
library(sf)
library(tidyverse)
library(purrr)
library(patchwork)
library(ncdf4)
Show the code
# # EU map
# box <- st_bbox(c(xmin = -20, xmax = 20, ymax = 45, ymin = 80),
#                 crs = st_crs(4326)) |> 
#   st_as_sfc() |> 
#   st_transform(3035)

eu_countries <- get_eurostat_geospatial(resolution = 10, 
                                        nuts_level = 0, 
                                        year = 2016,
                                        crs = "3035") 

Read data directly from the web (wiothout dowloading the data to a local folder

Show the code
#' Get the data of wind speed and direction at specific heihts from a raster
#' @param .r NORA3 data (SpatRaster)
#' @param .height Height to get the data (20, 50, 100, 250, 750)

get_wind_height <- function(.r, .height = 100){
  
  # Velocity vector (y, y)
  ux = subset(.r, paste0("x_wind_", .height, "m"))
  uy = subset(.r, paste0("y_wind_", .height, "m"))
  
  # Calculate magnityd (mag) and direction (dir)
  u_mag = sqrt(ux^2 + uy^2)
  names(u_mag) <- "magnitude"
  u_dir = terra::atan2(y = uy, x = ux) * 180/pi
  names(u_dir) <- "direction"

  # Generate raster 
  u = c(u_mag, u_dir) 
  
  # Output as one raster
  return(u)
  
}

# Function for dowloading wind data from NORA3 
get_wind_z <- function(.year,
                       .month,
                       .day,
                       .hour_group,
                       .lead_time){
  
  # URL of the data
  nora3_url <- paste0("https://thredds.met.no/thredds/dodsC/nora3/",
                      .year,
                      "/",
                      .month,
                      "/",
                      .day, 
                      "/",
                      .hour_group,
                      "/fc", 
                      .year,
                      .month, 
                      .day,
                      .hour_group,
                      "_",
                      .lead_time,
                      "_fp.nc")
  
  # Open the netCDF file
  ncin <- ncdf4::nc_open(nora3_url)
  
  # Get coordinate  variables
  lon <- ncdf4::ncvar_get(ncin,"x")
  lat <- ncdf4::ncvar_get(ncin,"y")
  
  # Get time
  time <- ncdf4::ncvar_get(ncin,"time")
  
  # Get wind speed at 10 m above ground (height4)
  dname <- "wind_speed"
  ws10_array <- ncdf4::ncvar_get(ncin,dname)
  dlname <- ncdf4::ncatt_get(ncin,dname,"standard_name")
  dunits <- ncdf4::ncatt_get(ncin,dname,"units")
  fillvalue <- ncdf4::ncatt_get(ncin,dname,"_FillValue")
  # replace netCDF fill values with NA's
  ws10_array[ws10_array == fillvalue$value] <- NA
  
  # Get wind direction at 10 m above ground (height4)
  dname <- "wind_direction"
  wd10_array <- ncdf4::ncvar_get(ncin,dname)
  dlname <- ncdf4::ncatt_get(ncin,dname,"standard_name")
  dunits <- ncdf4::ncatt_get(ncin,dname,"units")
  fillvalue <- ncdf4::ncatt_get(ncin,dname,"_FillValue")
  # replace netCDF fill values with NA's
  wd10_array[wd10_array == fillvalue$value] <- NA
  
  # x_wind_z[x,y,height2,time] 
  # ncdf4::ncvar_get(ncin,"height2") -- 20  50 100 250 500 750 m above ground
  dname <- "x_wind_z"
  xh2_array <- ncdf4::ncvar_get(ncin,dname)
  dlname <- ncdf4::ncatt_get(ncin,dname,"standard_name")
  dunits <- ncdf4::ncatt_get(ncin,dname,"units")
  fillvalue <- ncdf4::ncatt_get(ncin,dname,"_FillValue")
  # replace netCDF fill values with NA's
  xh2_array[xh2_array == fillvalue$value] <- NA
  
  # y_wind_z[x,y,height2,time]
  dname <- "y_wind_z"
  yh2_array <- ncdf4::ncvar_get(ncin,dname)
  dlname <- ncdf4::ncatt_get(ncin,dname,"standard_name")
  dunits <- ncdf4::ncatt_get(ncin,dname,"units")
  fillvalue <- ncdf4::ncatt_get(ncin,dname,"_FillValue")
  # replace netCDF fill values with NA's
  yh2_array[yh2_array == fillvalue$value] <- NA
  
  
  # create dataframe with values
  df <- expand.grid(lon,lat) |> 
    as_tibble() |> 
    dplyr::rename_with(~ c("lon", "lat"), 1:2) |> 
    # Add wind speed and direction at 10 m
    dplyr::mutate(wind10_mag = as.vector(ws10_array),
                  wind10_dir = as.vector(wd10_array)) |> 
    # Add wind speed at h2 
    dplyr::mutate(
      # x_wind_z
      x_wind_20m  = as.vector(xh2_array[ , , 1]),
      x_wind_50m  = as.vector(xh2_array[ , , 2]),
      x_wind_100m = as.vector(xh2_array[ , , 3]),
      x_wind_250m = as.vector(xh2_array[ , , 4]),
      x_wind_500m = as.vector(xh2_array[ , , 5]),
      x_wind_750m = as.vector(xh2_array[ , , 6]),
      # y_wind_z 
      y_wind_20m  = as.vector(yh2_array[ , , 1]),
      y_wind_50m  = as.vector(yh2_array[ , , 2]),
      y_wind_100m = as.vector(yh2_array[ , , 3]),
      y_wind_250m = as.vector(yh2_array[ , , 4]),
      y_wind_500m = as.vector(yh2_array[ , , 5]),
      y_wind_750m = as.vector(yh2_array[ , , 6])
    )
  
  # Create Raster with all data
  r_crs <- "+proj=lcc +lat_0=66.3 +lon_0=-42 +lat_1=66.3 +lat_2=66.3 +x_0=0 +y_0=0 +R=6371000 +units=m +no_defs"
  r <- tidyterra::as_spatraster(df, crs = r_crs, digits = 4)
  # Add time 
  time(r) <- rep(lubridate::as_datetime(time, tz = "UTC"), times = length(names(r)))
  
  # get wind speed and direction at all heights
  ff <- function(.height) { get_wind_height(r, .height) }
  u_height_list <- c(20, 50, 100, 250, 750)
  u_height <- map(u_height_list, ff)
  names(u_height) <- c("wind20", "wind50", "wind100", "wind250", "wind750")
  u_height <- u_height |>
    rast() |> 
    rename_with( ~ gsub("_1", "_mag", .x, fixed = TRUE)) |> 
    rename_with( ~ gsub("_2", "_dir", .x, fixed = TRUE))
  
  # Retrurn SpatRaster object
  rr <- c(tidyterra::select(r, wind10_mag,  wind10_dir), u_height)
  
  return(rr)
  
} 
Show the code
wind_nora3 <- get_wind_z(.year = "2018",
                         .month = "11",
                         .day = "08",
                         .hour_group = "00",
                         .lead_time = "004")

wind_nora3
class       : SpatRaster 
dimensions  : 1489, 889, 12  (nrow, ncol, nlyr)
resolution  : 2999.875, 2999.969  (x, y)
extent      : 776860.9, 3443750, -1271977, 3194976  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=lcc +lat_0=66.3 +lon_0=-42 +lat_1=66.3 +lat_2=66.3 +x_0=0 +y_0=0 +R=6371000 +units=m +no_defs 
source(s)   : memory
names       :   wind10_mag,   wind10_dir,   wind20_mag, wind20_dir,   wind50_mag, wind50_dir, ... 
min values  :  0.004474435, 3.791680e-04,  0.003715257,  -179.9983,  0.006306755,  -179.9968, ... 
max values  : 26.079982758, 3.599998e+02, 27.383629015,   179.9978, 29.652804073,   179.9996, ... 
time        : 2018-11-08 04:00:00 UTC 
Show the code
ggplot() +
  geom_spatraster(data =  select(wind_nora3 , ends_with("mag"))) +
  facet_wrap(~lyr, ncol = 2) +
  scale_fill_whitebox_c(name = "u [m/s]", palette = "viridi") +
  geom_sf(data = eu_countries, fill = NA, colour = "white") +
  coord_sf(expand = FALSE, crs = sf::st_crs(3035)) +
  scale_x_continuous(limits = c(3000000, 5000000)) +
  scale_y_continuous(limits = c(3600000, 5300000)) +
  labs(title = terra::time(wind_nora3)[1]) +
  theme_bw()
Figure 4

Extract values from one point (e.g., near Utsira - Coordinates from google maps [WGS84]: 59.346588 (lat), 4.899842 (long)).

Show the code
# Point 
xy <- cbind(4.899842, 59.346588)
point <- vect(xy, crs="+proj=longlat +datum=WGS84") |> 
  project(crs(wind_nora3))

# Extract values
p_wind <- terra::extract(wind_nora3, point) |>  
  select(ends_with("_mag")) |> 
  pivot_longer(cols = everything()) |> 
  rename(height_m = name,
         wind_m_s = value) |> 
  mutate(height_m = readr::parse_number(height_m))
  

# Plot
ggplot() + 
  geom_point(data = p_wind,
             aes(x = wind_m_s, y = height_m ),
             col = "blue") +
  labs(x = "u [m/s]",
       y = "height [m]",
       title = "Vertical wind profile at point: 59.346588 (lat), 4.899842 (long)", 
       subtitle = terra::time(wind_nora3)[1]) +
  theme_bw()

Interpolation wind profile (Solbrekke and Sorteberg 2022):

  • Exponential relation:

    \[ u_{z_2}(t) = u_{z_1}(t) \Big(\frac{z_2}{z_1}\Big)^{\alpha(t)} \]

  • Exponential power law coefficient:

    \[ \alpha(t) = \frac{ln\Big( \frac{u_{z_2}(t)}{u_{z_1}(t)} \Big)}{ln\Big( \frac{z_2}{z_1} \Big)} \]

Show the code
get_inter_wind_profile <- function(x){
  
  # Vector with alpha depending on height
  alpha <- rep(NA, 5)
  for(i in seq_along(alpha)) {
    alpha[i] = as.numeric( (log(x[i, 2]/x[i+1, 2]) / log(x[i, 1]/x[i+1, 1])) )
  }
  
  # Create data frame for interpolation (steps = 1) 
  wind_inter <- tibble(height_m = seq(10, 750, 1)) |> 
    mutate(wind_m_s = NA) |> 
    mutate(alpha = case_when(
      height_m <= 20 ~ alpha[1],
      height_m >  20 &  height_m <=  50 ~ alpha[2],
      height_m >  50 &  height_m <= 100 ~ alpha[3],
      height_m > 100 &  height_m <= 250 ~ alpha[4],
      height_m > 250 &  height_m <= 750 ~ alpha[5]
    ))  
  
  # Interpolation (based on u [m/s] at 10 m)
  wind_inter$wind_m_s[1] = x[x["height_m"] == 10, ]$wind_m_s
  for(i in 2:length(wind_inter$height_m)) {
    wind_inter$wind_m_s[i] = wind_inter$wind_m_s[i-1] * (wind_inter$height_m[i] / wind_inter$height_m[i-1])^wind_inter$alpha[i]
  }
  
  return(wind_inter)
  
}
  
wind_inter <- get_inter_wind_profile(p_wind)

# Plot
ggplot() + 
  geom_point(data = p_wind,
             aes(x = wind_m_s, y = height_m ),
             col = "blue") +
  geom_line(data = wind_inter,
             aes(x = wind_m_s, y = height_m ),
             col = "red") + 
  labs(x = "u [m/s]",
       y = "height [m]",
       title = "Vertical wind profile at point: 59.346588 (lat), 4.899842 (long)", 
       subtitle = terra::time(wind_nora3)[1]) +
  theme_bw()

Exercise 4

Objetives

Time series wind using NORA3 data

Analyse wind speed and direction over time in a point at 100 m hight.

Solution

Show the code
library(terra)
library(tidyterra)
library(eurostat)
library(sf)
library(tidyverse)
library(lubridate)
library(photobiology)
library(purrr)
library(patchwork)
library(ncdf4)
library(gt)

Read data that have been previously downloaded into a local file.

Show the code
wind_100m <- readRDS("data/wind_100m.rds") 

wind_100m <- wind_100m |> 
  # Add columns indicatind day/night time
  mutate(day_night = ifelse(is_daytime(time, tz = "Europe/Oslo"), "day", "night"))

wind_100m |>
  gt()
year month day hour_group lead_time long lat ID u_mag u_dir time day_night
2018 01 01 00 004 4.899842 59.34659 1 13.4393594 105.3728971 2018-01-01 04:00:00 night
2018 01 01 00 005 4.899842 59.34659 1 13.3255420 100.5657463 2018-01-01 05:00:00 night
2018 01 01 00 006 4.899842 59.34659 1 13.5051916 99.1950790 2018-01-01 06:00:00 night
2018 01 01 00 007 4.899842 59.34659 1 11.2424514 81.8105987 2018-01-01 07:00:00 night
2018 01 01 00 008 4.899842 59.34659 1 11.2823779 85.8412149 2018-01-01 08:00:00 night
2018 01 01 00 009 4.899842 59.34659 1 10.7900183 72.5036945 2018-01-01 09:00:00 day
2018 01 01 06 004 4.899842 59.34659 1 7.4036502 13.5743332 2018-01-01 10:00:00 day
2018 01 01 06 005 4.899842 59.34659 1 8.0308436 10.8492618 2018-01-01 11:00:00 day
2018 01 01 06 006 4.899842 59.34659 1 9.0159255 6.9195379 2018-01-01 12:00:00 day
2018 01 01 06 007 4.899842 59.34659 1 7.7525221 13.5502902 2018-01-01 13:00:00 day
2018 01 01 06 008 4.899842 59.34659 1 7.8558757 5.0274267 2018-01-01 14:00:00 day
2018 01 01 06 009 4.899842 59.34659 1 7.1992725 3.2982519 2018-01-01 15:00:00 day
2018 01 01 12 004 4.899842 59.34659 1 9.1077356 -0.2239485 2018-01-01 16:00:00 night
2018 01 01 12 005 4.899842 59.34659 1 9.5103237 2.1884164 2018-01-01 17:00:00 night
2018 01 01 12 006 4.899842 59.34659 1 9.9928377 2.1185059 2018-01-01 18:00:00 night
2018 01 01 12 007 4.899842 59.34659 1 10.1710031 4.2324802 2018-01-01 19:00:00 night
2018 01 01 12 008 4.899842 59.34659 1 9.9837195 2.2040420 2018-01-01 20:00:00 night
2018 01 01 12 009 4.899842 59.34659 1 9.6288718 1.2356494 2018-01-01 21:00:00 night
2018 01 01 18 004 4.899842 59.34659 1 9.6354665 2.7036963 2018-01-01 22:00:00 night
2018 01 01 18 005 4.899842 59.34659 1 9.6891242 3.8853679 2018-01-01 23:00:00 night
2018 01 01 18 006 4.899842 59.34659 1 9.4839699 1.3019254 2018-01-02 night
2018 01 01 18 007 4.899842 59.34659 1 8.7628428 0.3324338 2018-01-02 01:00:00 night
2018 01 01 18 008 4.899842 59.34659 1 8.2817781 3.2307277 2018-01-02 02:00:00 night
2018 01 01 18 009 4.899842 59.34659 1 7.9362150 -0.6433558 2018-01-02 03:00:00 night
2018 01 02 00 004 4.899842 59.34659 1 8.0583002 -1.7832517 2018-01-02 04:00:00 night
2018 01 02 00 005 4.899842 59.34659 1 7.5283060 -1.4084536 2018-01-02 05:00:00 night
2018 01 02 00 006 4.899842 59.34659 1 6.8727441 1.4844225 2018-01-02 06:00:00 night
2018 01 02 00 007 4.899842 59.34659 1 6.3204620 6.4400305 2018-01-02 07:00:00 night
2018 01 02 00 008 4.899842 59.34659 1 5.8913214 9.5355364 2018-01-02 08:00:00 night
2018 01 02 00 009 4.899842 59.34659 1 4.7071529 13.1489744 2018-01-02 09:00:00 day
2018 01 02 06 004 4.899842 59.34659 1 3.7404010 25.0828643 2018-01-02 10:00:00 day
2018 01 02 06 005 4.899842 59.34659 1 3.5810539 41.9025410 2018-01-02 11:00:00 day
2018 01 02 06 006 4.899842 59.34659 1 3.5064393 50.4021880 2018-01-02 12:00:00 day
2018 01 02 06 007 4.899842 59.34659 1 4.2846127 64.4093018 2018-01-02 13:00:00 day
2018 01 02 06 008 4.899842 59.34659 1 2.4497540 86.6091039 2018-01-02 14:00:00 day
2018 01 02 06 009 4.899842 59.34659 1 4.3818628 107.8187668 2018-01-02 15:00:00 day
2018 01 02 12 004 4.899842 59.34659 1 4.7289017 122.5705474 2018-01-02 16:00:00 night
2018 01 02 12 005 4.899842 59.34659 1 5.5337164 127.2560564 2018-01-02 17:00:00 night
2018 01 02 12 006 4.899842 59.34659 1 6.1743297 136.5981852 2018-01-02 18:00:00 night
2018 01 02 12 007 4.899842 59.34659 1 7.3246164 148.0861042 2018-01-02 19:00:00 night
2018 01 02 12 008 4.899842 59.34659 1 8.8395053 151.7909553 2018-01-02 20:00:00 night
2018 01 02 12 009 4.899842 59.34659 1 10.0588595 163.8801560 2018-01-02 21:00:00 night
2018 01 02 18 004 4.899842 59.34659 1 11.7548057 179.8165908 2018-01-02 22:00:00 night
2018 01 02 18 005 4.899842 59.34659 1 11.9327245 -177.1665976 2018-01-02 23:00:00 night
2018 01 02 18 006 4.899842 59.34659 1 12.1709078 -174.0704402 2018-01-03 night
2018 01 02 18 007 4.899842 59.34659 1 13.5692188 -178.1050272 2018-01-03 01:00:00 night
2018 01 02 18 008 4.899842 59.34659 1 12.8353008 -176.3596484 2018-01-03 02:00:00 night
2018 01 02 18 009 4.899842 59.34659 1 12.0062975 -161.5028788 2018-01-03 03:00:00 night
2018 01 03 00 004 4.899842 59.34659 1 11.4380405 -155.3844896 2018-01-03 04:00:00 night
2018 01 03 00 005 4.899842 59.34659 1 10.9728374 -144.6271965 2018-01-03 05:00:00 night
2018 01 03 00 006 4.899842 59.34659 1 9.6806672 -140.9868817 2018-01-03 06:00:00 night
2018 01 03 00 007 4.899842 59.34659 1 8.6439929 -140.5788210 2018-01-03 07:00:00 night
2018 01 03 00 008 4.899842 59.34659 1 7.1499628 -117.1284066 2018-01-03 08:00:00 night
2018 01 03 00 009 4.899842 59.34659 1 7.7838097 -109.6204521 2018-01-03 09:00:00 day
2018 01 03 06 004 4.899842 59.34659 1 8.9428845 -103.7537081 2018-01-03 10:00:00 day
2018 01 03 06 005 4.899842 59.34659 1 9.3042986 -101.4344305 2018-01-03 11:00:00 day
2018 01 03 06 006 4.899842 59.34659 1 9.6583382 -94.4485039 2018-01-03 12:00:00 day
2018 01 03 06 007 4.899842 59.34659 1 9.3765114 -97.5950685 2018-01-03 13:00:00 day
2018 01 03 06 008 4.899842 59.34659 1 7.9441262 -100.7920868 2018-01-03 14:00:00 day
2018 01 03 06 009 4.899842 59.34659 1 7.0033566 -102.4070942 2018-01-03 15:00:00 day
2018 01 03 12 004 4.899842 59.34659 1 9.2016386 -96.5586314 2018-01-03 16:00:00 night
2018 01 03 12 005 4.899842 59.34659 1 7.6875175 -98.8389378 2018-01-03 17:00:00 night
2018 01 03 12 006 4.899842 59.34659 1 6.1868378 -103.3415169 2018-01-03 18:00:00 night
2018 01 03 12 007 4.899842 59.34659 1 4.9667468 -108.0304943 2018-01-03 19:00:00 night
2018 01 03 12 008 4.899842 59.34659 1 5.3295327 -108.0161626 2018-01-03 20:00:00 night
2018 01 03 12 009 4.899842 59.34659 1 5.2799468 -102.1967813 2018-01-03 21:00:00 night
2018 01 03 18 004 4.899842 59.34659 1 7.0256463 -112.5890615 2018-01-03 22:00:00 night
2018 01 03 18 005 4.899842 59.34659 1 6.4315630 -121.3735685 2018-01-03 23:00:00 night
2018 01 03 18 006 4.899842 59.34659 1 6.4885012 -130.8004783 2018-01-04 night
2018 01 03 18 007 4.899842 59.34659 1 4.8255426 -125.0452004 2018-01-04 01:00:00 night
2018 01 03 18 008 4.899842 59.34659 1 3.7325165 -122.6689825 2018-01-04 02:00:00 night
2018 01 03 18 009 4.899842 59.34659 1 2.9611050 -122.3318374 2018-01-04 03:00:00 night
2018 01 04 00 004 4.899842 59.34659 1 6.0586949 -99.1981224 2018-01-04 04:00:00 night
2018 01 04 00 005 4.899842 59.34659 1 5.8850520 -102.5572154 2018-01-04 05:00:00 night
2018 01 04 00 006 4.899842 59.34659 1 4.7108418 -117.3612611 2018-01-04 06:00:00 night
2018 01 04 00 007 4.899842 59.34659 1 4.1915827 -124.4108225 2018-01-04 07:00:00 night
2018 01 04 00 008 4.899842 59.34659 1 4.1119075 -127.1962250 2018-01-04 08:00:00 night
2018 01 04 00 009 4.899842 59.34659 1 4.8670429 -128.6301839 2018-01-04 09:00:00 day
2018 01 04 06 004 4.899842 59.34659 1 5.8102671 -133.2979209 2018-01-04 10:00:00 day
2018 01 04 06 005 4.899842 59.34659 1 4.8776319 -151.8410353 2018-01-04 11:00:00 day
2018 01 04 06 006 4.899842 59.34659 1 4.6805715 -159.9302724 2018-01-04 12:00:00 day
2018 01 04 06 007 4.899842 59.34659 1 5.7089988 -173.2597185 2018-01-04 13:00:00 day
2018 01 04 06 008 4.899842 59.34659 1 6.0838413 -175.0490322 2018-01-04 14:00:00 day
2018 01 04 06 009 4.899842 59.34659 1 5.6045673 -168.4802642 2018-01-04 15:00:00 day
2018 01 04 12 004 4.899842 59.34659 1 5.7287654 -155.4345061 2018-01-04 16:00:00 night
2018 01 04 12 005 4.899842 59.34659 1 5.1201096 -155.4478123 2018-01-04 17:00:00 night
2018 01 04 12 006 4.899842 59.34659 1 4.8979129 -143.8133348 2018-01-04 18:00:00 night
2018 01 04 12 007 4.899842 59.34659 1 5.0777394 -132.4595309 2018-01-04 19:00:00 night
2018 01 04 12 008 4.899842 59.34659 1 4.4910341 -136.9277618 2018-01-04 20:00:00 night
2018 01 04 12 009 4.899842 59.34659 1 3.4905239 -130.7218893 2018-01-04 21:00:00 night
2018 01 04 18 004 4.899842 59.34659 1 4.5447734 -112.5000350 2018-01-04 22:00:00 night
2018 01 04 18 005 4.899842 59.34659 1 4.2134023 -112.1594423 2018-01-04 23:00:00 night
2018 01 04 18 006 4.899842 59.34659 1 4.6769542 -110.0077171 2018-01-05 night
2018 01 04 18 007 4.899842 59.34659 1 3.6139780 -105.7770235 2018-01-05 01:00:00 night
2018 01 04 18 008 4.899842 59.34659 1 3.7151901 -86.0969984 2018-01-05 02:00:00 night
2018 01 04 18 009 4.899842 59.34659 1 4.4537730 -88.1786419 2018-01-05 03:00:00 night
2018 01 05 00 004 4.899842 59.34659 1 5.5489703 -88.3580593 2018-01-05 04:00:00 night
2018 01 05 00 005 4.899842 59.34659 1 4.4415259 -85.4711447 2018-01-05 05:00:00 night
2018 01 05 00 006 4.899842 59.34659 1 3.3331542 -83.7109613 2018-01-05 06:00:00 night
2018 01 05 00 007 4.899842 59.34659 1 3.1458131 -97.3173301 2018-01-05 07:00:00 night
2018 01 05 00 008 4.899842 59.34659 1 2.4643512 -104.4196540 2018-01-05 08:00:00 night
2018 01 05 00 009 4.899842 59.34659 1 2.7836981 -108.4544148 2018-01-05 09:00:00 day
2018 01 05 06 004 4.899842 59.34659 1 3.7178263 -81.8754846 2018-01-05 10:00:00 day
2018 01 05 06 005 4.899842 59.34659 1 3.1105651 -123.1721421 2018-01-05 11:00:00 day
2018 01 05 06 006 4.899842 59.34659 1 3.3225617 -160.5752324 2018-01-05 12:00:00 day
2018 01 05 06 007 4.899842 59.34659 1 2.7137617 176.3080747 2018-01-05 13:00:00 day
2018 01 05 06 008 4.899842 59.34659 1 2.7918153 171.3827850 2018-01-05 14:00:00 day
2018 01 05 06 009 4.899842 59.34659 1 2.7778055 166.9924086 2018-01-05 15:00:00 day
2018 01 05 12 004 4.899842 59.34659 1 0.7968741 -78.1715192 2018-01-05 16:00:00 night
2018 01 05 12 005 4.899842 59.34659 1 0.8266747 -151.7344133 2018-01-05 17:00:00 night
2018 01 05 12 006 4.899842 59.34659 1 1.2806361 -179.1718807 2018-01-05 18:00:00 night
2018 01 05 12 007 4.899842 59.34659 1 0.4865575 -134.8996261 2018-01-05 19:00:00 night
2018 01 05 12 008 4.899842 59.34659 1 1.4327982 -70.4381171 2018-01-05 20:00:00 night
2018 01 05 12 009 4.899842 59.34659 1 2.1203856 -64.2770989 2018-01-05 21:00:00 night
2018 01 05 18 004 4.899842 59.34659 1 4.5835482 -37.6033878 2018-01-05 22:00:00 night
2018 01 05 18 005 4.899842 59.34659 1 4.9567464 -36.5121994 2018-01-05 23:00:00 night
2018 01 05 18 006 4.899842 59.34659 1 6.9401152 -34.9610600 2018-01-06 night
2018 01 05 18 007 4.899842 59.34659 1 11.7955986 -47.7712222 2018-01-06 01:00:00 night
2018 01 05 18 008 4.899842 59.34659 1 13.3045512 -51.0694266 2018-01-06 02:00:00 night
2018 01 05 18 009 4.899842 59.34659 1 14.1802154 -50.2742132 2018-01-06 03:00:00 night
2018 01 06 00 004 4.899842 59.34659 1 11.8539891 -55.5560091 2018-01-06 04:00:00 night
2018 01 06 00 005 4.899842 59.34659 1 11.8503852 -56.8267369 2018-01-06 05:00:00 night
2018 01 06 00 006 4.899842 59.34659 1 12.3296398 -61.1956274 2018-01-06 06:00:00 night
2018 01 06 00 007 4.899842 59.34659 1 10.9836402 -68.6821347 2018-01-06 07:00:00 night
2018 01 06 00 008 4.899842 59.34659 1 10.0745509 -67.4908056 2018-01-06 08:00:00 night
2018 01 06 00 009 4.899842 59.34659 1 9.8474303 -62.3296784 2018-01-06 09:00:00 day
2018 01 06 06 004 4.899842 59.34659 1 11.1148422 -55.8201657 2018-01-06 10:00:00 day
2018 01 06 06 005 4.899842 59.34659 1 12.1388800 -51.9165448 2018-01-06 11:00:00 day
2018 01 06 06 006 4.899842 59.34659 1 13.3910060 -49.0835808 2018-01-06 12:00:00 day
2018 01 06 06 007 4.899842 59.34659 1 12.6857600 -50.4880195 2018-01-06 13:00:00 day
2018 01 06 06 008 4.899842 59.34659 1 12.3438330 -53.6416428 2018-01-06 14:00:00 day
2018 01 06 06 009 4.899842 59.34659 1 12.4576439 -52.0348933 2018-01-06 15:00:00 day
2018 01 06 12 004 4.899842 59.34659 1 10.0821936 -51.5760198 2018-01-06 16:00:00 day
2018 01 06 12 005 4.899842 59.34659 1 10.1254665 -54.7253073 2018-01-06 17:00:00 night
2018 01 06 12 006 4.899842 59.34659 1 10.8768196 -53.2075231 2018-01-06 18:00:00 night
2018 01 06 12 007 4.899842 59.34659 1 10.7950570 -53.2290367 2018-01-06 19:00:00 night
2018 01 06 12 008 4.899842 59.34659 1 10.5674593 -55.0127038 2018-01-06 20:00:00 night
2018 01 06 12 009 4.899842 59.34659 1 8.8388816 -61.6410825 2018-01-06 21:00:00 night
2018 01 06 18 004 4.899842 59.34659 1 7.5040679 -49.0575608 2018-01-06 22:00:00 night
2018 01 06 18 005 4.899842 59.34659 1 6.2151854 -55.7452003 2018-01-06 23:00:00 night
2018 01 06 18 006 4.899842 59.34659 1 4.2384605 -79.5528356 2018-01-07 night
2018 01 06 18 007 4.899842 59.34659 1 2.2198107 -101.7772373 2018-01-07 01:00:00 night
2018 01 06 18 008 4.899842 59.34659 1 1.6637324 -174.3946618 2018-01-07 02:00:00 night
2018 01 06 18 009 4.899842 59.34659 1 2.0405656 127.1030565 2018-01-07 03:00:00 night
2018 01 07 00 004 4.899842 59.34659 1 4.8087240 50.4702059 2018-01-07 04:00:00 night
2018 01 07 00 005 4.899842 59.34659 1 5.8384787 50.4679636 2018-01-07 05:00:00 night
2018 01 07 00 006 4.899842 59.34659 1 6.5808418 55.9392229 2018-01-07 06:00:00 night
2018 01 07 00 007 4.899842 59.34659 1 7.8513285 59.9253105 2018-01-07 07:00:00 night
2018 01 07 00 008 4.899842 59.34659 1 8.9390804 65.4823071 2018-01-07 08:00:00 night
2018 01 07 00 009 4.899842 59.34659 1 10.9099635 72.5028781 2018-01-07 09:00:00 day
2018 01 07 06 004 4.899842 59.34659 1 12.5303391 64.4007553 2018-01-07 10:00:00 day
2018 01 07 06 005 4.899842 59.34659 1 14.4269911 65.8557966 2018-01-07 11:00:00 day
2018 01 07 06 006 4.899842 59.34659 1 15.7242339 60.0687802 2018-01-07 12:00:00 day
2018 01 07 06 007 4.899842 59.34659 1 16.2590679 53.9684954 2018-01-07 13:00:00 day
2018 01 07 06 008 4.899842 59.34659 1 16.7224407 49.7631781 2018-01-07 14:00:00 day
2018 01 07 06 009 4.899842 59.34659 1 16.1227728 46.4987752 2018-01-07 15:00:00 day
2018 01 07 12 004 4.899842 59.34659 1 16.4044671 44.9287004 2018-01-07 16:00:00 day
2018 01 07 12 005 4.899842 59.34659 1 15.6186615 43.7540981 2018-01-07 17:00:00 night
2018 01 07 12 006 4.899842 59.34659 1 14.4705616 42.2503125 2018-01-07 18:00:00 night
2018 01 07 12 007 4.899842 59.34659 1 14.0194586 41.2838297 2018-01-07 19:00:00 night
2018 01 07 12 008 4.899842 59.34659 1 13.2921381 40.9935583 2018-01-07 20:00:00 night
2018 01 07 12 009 4.899842 59.34659 1 12.2931160 44.0816486 2018-01-07 21:00:00 night
2018 01 07 18 004 4.899842 59.34659 1 11.1508259 35.8653087 2018-01-07 22:00:00 night
2018 01 07 18 005 4.899842 59.34659 1 9.5573464 36.8940106 2018-01-07 23:00:00 night
2018 01 07 18 006 4.899842 59.34659 1 7.9562834 43.1045668 2018-01-08 night
2018 01 07 18 007 4.899842 59.34659 1 7.0790128 54.2111943 2018-01-08 01:00:00 night
2018 01 07 18 008 4.899842 59.34659 1 6.8346431 62.6467392 2018-01-08 02:00:00 night
2018 01 07 18 009 4.899842 59.34659 1 6.4543540 70.2285909 2018-01-08 03:00:00 night
2018 01 08 00 004 4.899842 59.34659 1 6.9366103 91.7568422 2018-01-08 04:00:00 night
2018 01 08 00 005 4.899842 59.34659 1 6.7032924 91.4971668 2018-01-08 05:00:00 night
2018 01 08 00 006 4.899842 59.34659 1 6.2855655 92.7599329 2018-01-08 06:00:00 night
2018 01 08 00 007 4.899842 59.34659 1 5.6388049 96.6331808 2018-01-08 07:00:00 night
2018 01 08 00 008 4.899842 59.34659 1 5.4803240 102.3457739 2018-01-08 08:00:00 night
2018 01 08 00 009 4.899842 59.34659 1 5.9506504 106.0683985 2018-01-08 09:00:00 day
2018 01 08 06 004 4.899842 59.34659 1 5.8774331 109.0361140 2018-01-08 10:00:00 day
2018 01 08 06 005 4.899842 59.34659 1 5.8040716 120.2039159 2018-01-08 11:00:00 day
2018 01 08 06 006 4.899842 59.34659 1 6.5855676 127.8896659 2018-01-08 12:00:00 day
2018 01 08 06 007 4.899842 59.34659 1 6.6265308 130.1749599 2018-01-08 13:00:00 day
2018 01 08 06 008 4.899842 59.34659 1 6.2167132 130.8595390 2018-01-08 14:00:00 day
2018 01 08 06 009 4.899842 59.34659 1 5.6648564 137.1267510 2018-01-08 15:00:00 day
2018 01 08 12 004 4.899842 59.34659 1 5.8317201 150.3017542 2018-01-08 16:00:00 day
2018 01 08 12 005 4.899842 59.34659 1 6.7354917 158.2936535 2018-01-08 17:00:00 night
2018 01 08 12 006 4.899842 59.34659 1 8.5859532 155.2638177 2018-01-08 18:00:00 night
2018 01 08 12 007 4.899842 59.34659 1 9.2544399 148.8194193 2018-01-08 19:00:00 night
2018 01 08 12 008 4.899842 59.34659 1 9.6886700 151.9108566 2018-01-08 20:00:00 night
2018 01 08 12 009 4.899842 59.34659 1 10.1714799 162.6006329 2018-01-08 21:00:00 night
2018 01 08 18 004 4.899842 59.34659 1 10.6025254 161.9337098 2018-01-08 22:00:00 night
2018 01 08 18 005 4.899842 59.34659 1 10.7549572 162.8144604 2018-01-08 23:00:00 night
2018 01 08 18 006 4.899842 59.34659 1 11.8828517 160.3873821 2018-01-09 night
2018 01 08 18 007 4.899842 59.34659 1 11.8221177 159.9648512 2018-01-09 01:00:00 night
2018 01 08 18 008 4.899842 59.34659 1 12.4005348 163.4464628 2018-01-09 02:00:00 night
2018 01 08 18 009 4.899842 59.34659 1 12.8081524 166.9986554 2018-01-09 03:00:00 night
2018 01 09 00 004 4.899842 59.34659 1 13.0545386 172.9288355 2018-01-09 04:00:00 night
2018 01 09 00 005 4.899842 59.34659 1 13.2287273 173.6410883 2018-01-09 05:00:00 night
2018 01 09 00 006 4.899842 59.34659 1 13.4845059 176.4512072 2018-01-09 06:00:00 night
2018 01 09 00 007 4.899842 59.34659 1 13.4685260 -178.9011732 2018-01-09 07:00:00 night
2018 01 09 00 008 4.899842 59.34659 1 13.9169409 -176.9931788 2018-01-09 08:00:00 night
2018 01 09 00 009 4.899842 59.34659 1 14.3269205 -177.5574457 2018-01-09 09:00:00 day
2018 01 09 06 004 4.899842 59.34659 1 14.3451135 -177.5104021 2018-01-09 10:00:00 day
2018 01 09 06 005 4.899842 59.34659 1 13.8986666 -179.0356555 2018-01-09 11:00:00 day
2018 01 09 06 006 4.899842 59.34659 1 13.3991934 179.8374029 2018-01-09 12:00:00 day
2018 01 09 06 007 4.899842 59.34659 1 13.7868714 -179.5073398 2018-01-09 13:00:00 day
2018 01 09 06 008 4.899842 59.34659 1 14.2526489 -177.6876339 2018-01-09 14:00:00 day
2018 01 09 06 009 4.899842 59.34659 1 15.2314251 -175.9015668 2018-01-09 15:00:00 day
2018 01 09 12 004 4.899842 59.34659 1 16.1458319 -168.6977984 2018-01-09 16:00:00 day
2018 01 09 12 005 4.899842 59.34659 1 16.2047403 -168.9802124 2018-01-09 17:00:00 night
2018 01 09 12 006 4.899842 59.34659 1 16.2250052 -171.5013615 2018-01-09 18:00:00 night
2018 01 09 12 007 4.899842 59.34659 1 16.0259513 -173.0592756 2018-01-09 19:00:00 night
2018 01 09 12 008 4.899842 59.34659 1 15.8614271 -171.7753288 2018-01-09 20:00:00 night
2018 01 09 12 009 4.899842 59.34659 1 14.6340079 -170.3575411 2018-01-09 21:00:00 night
2018 01 09 18 004 4.899842 59.34659 1 13.0895093 -177.3417580 2018-01-09 22:00:00 night
2018 01 09 18 005 4.899842 59.34659 1 11.8716349 -177.9696025 2018-01-09 23:00:00 night
2018 01 09 18 006 4.899842 59.34659 1 11.7789357 -174.4242798 2018-01-10 night
2018 01 09 18 007 4.899842 59.34659 1 12.8616257 -178.9956234 2018-01-10 01:00:00 night
2018 01 09 18 008 4.899842 59.34659 1 13.2824812 -179.6146160 2018-01-10 02:00:00 night
2018 01 09 18 009 4.899842 59.34659 1 12.5286503 -178.1703052 2018-01-10 03:00:00 night
2018 01 10 00 004 4.899842 59.34659 1 11.6001177 178.1709884 2018-01-10 04:00:00 night
2018 01 10 00 005 4.899842 59.34659 1 11.6040457 175.0001088 2018-01-10 05:00:00 night
2018 01 10 00 006 4.899842 59.34659 1 12.1815967 170.9582024 2018-01-10 06:00:00 night
2018 01 10 00 007 4.899842 59.34659 1 11.5077119 170.6861933 2018-01-10 07:00:00 night
2018 01 10 00 008 4.899842 59.34659 1 11.3913879 173.1204611 2018-01-10 08:00:00 night
2018 01 10 00 009 4.899842 59.34659 1 11.1711665 168.2475131 2018-01-10 09:00:00 day
2018 01 10 06 004 4.899842 59.34659 1 8.8934929 177.7310467 2018-01-10 10:00:00 day
2018 01 10 06 005 4.899842 59.34659 1 7.5467294 173.8683496 2018-01-10 11:00:00 day
2018 01 10 06 006 4.899842 59.34659 1 7.4881586 171.4861379 2018-01-10 12:00:00 day
2018 01 10 06 007 4.899842 59.34659 1 8.8015472 178.1087966 2018-01-10 13:00:00 day
2018 01 10 06 008 4.899842 59.34659 1 9.5524769 175.8795953 2018-01-10 14:00:00 day
2018 01 10 06 009 4.899842 59.34659 1 9.5866413 -177.4214722 2018-01-10 15:00:00 day
2018 01 10 12 004 4.899842 59.34659 1 8.0150640 165.7097328 2018-01-10 16:00:00 day
2018 01 10 12 005 4.899842 59.34659 1 9.9366639 165.8126537 2018-01-10 17:00:00 night
2018 01 10 12 006 4.899842 59.34659 1 8.4349196 -174.6591545 2018-01-10 18:00:00 night
2018 01 10 12 007 4.899842 59.34659 1 7.6106692 -173.0027910 2018-01-10 19:00:00 night
2018 01 10 12 008 4.899842 59.34659 1 7.0112545 -164.8085532 2018-01-10 20:00:00 night
2018 01 10 12 009 4.899842 59.34659 1 7.3698723 -164.0847166 2018-01-10 21:00:00 night
2018 01 10 18 004 4.899842 59.34659 1 7.5073004 -167.6945907 2018-01-10 22:00:00 night
2018 01 10 18 005 4.899842 59.34659 1 6.5881605 -161.6710527 2018-01-10 23:00:00 night
2018 01 10 18 006 4.899842 59.34659 1 6.5845263 -167.4103314 2018-01-11 night
2018 01 10 18 007 4.899842 59.34659 1 5.8734672 -172.7972563 2018-01-11 01:00:00 night
2018 01 10 18 008 4.899842 59.34659 1 6.3627256 -179.6115558 2018-01-11 02:00:00 night
2018 01 10 18 009 4.899842 59.34659 1 6.8097449 177.9795830 2018-01-11 03:00:00 night
2018 01 11 00 004 4.899842 59.34659 1 4.6239140 -172.3674096 2018-01-11 04:00:00 night
2018 01 11 00 005 4.899842 59.34659 1 4.6363947 -170.4764528 2018-01-11 05:00:00 night
2018 01 11 00 006 4.899842 59.34659 1 5.1039439 -173.4069948 2018-01-11 06:00:00 night
2018 01 11 00 007 4.899842 59.34659 1 5.2128682 173.6704021 2018-01-11 07:00:00 night
2018 01 11 00 008 4.899842 59.34659 1 4.8536608 160.4426809 2018-01-11 08:00:00 night
2018 01 11 00 009 4.899842 59.34659 1 4.7842865 152.4169931 2018-01-11 09:00:00 day
2018 01 11 06 004 4.899842 59.34659 1 3.5364599 163.5006294 2018-01-11 10:00:00 day
2018 01 11 06 005 4.899842 59.34659 1 5.8941499 146.8039313 2018-01-11 11:00:00 day
2018 01 11 06 006 4.899842 59.34659 1 8.3387597 144.2264594 2018-01-11 12:00:00 day
2018 01 11 06 007 4.899842 59.34659 1 8.1622644 149.3325267 2018-01-11 13:00:00 day
2018 01 11 06 008 4.899842 59.34659 1 7.9289852 150.2263216 2018-01-11 14:00:00 day
2018 01 11 06 009 4.899842 59.34659 1 6.8269863 152.0244061 2018-01-11 15:00:00 day
2018 01 11 12 004 4.899842 59.34659 1 5.3359557 135.3091828 2018-01-11 16:00:00 day
2018 01 11 12 005 4.899842 59.34659 1 5.8077994 139.5098115 2018-01-11 17:00:00 night
2018 01 11 12 006 4.899842 59.34659 1 5.3490560 136.6607125 2018-01-11 18:00:00 night
2018 01 11 12 007 4.899842 59.34659 1 5.3597120 132.6959265 2018-01-11 19:00:00 night
2018 01 11 12 008 4.899842 59.34659 1 5.9898146 122.5858535 2018-01-11 20:00:00 night
2018 01 11 12 009 4.899842 59.34659 1 5.7765831 117.2516636 2018-01-11 21:00:00 night
2018 01 11 18 004 4.899842 59.34659 1 4.8033295 115.9971056 2018-01-11 22:00:00 night
2018 01 11 18 005 4.899842 59.34659 1 5.2763220 113.2564748 2018-01-11 23:00:00 night
2018 01 11 18 006 4.899842 59.34659 1 5.0092551 122.3211287 2018-01-12 night
2018 01 11 18 007 4.899842 59.34659 1 5.2978485 118.7117585 2018-01-12 01:00:00 night
2018 01 11 18 008 4.899842 59.34659 1 5.2995359 134.8267943 2018-01-12 02:00:00 night
2018 01 11 18 009 4.899842 59.34659 1 5.6776082 149.3934819 2018-01-12 03:00:00 night
2018 01 12 00 004 4.899842 59.34659 1 3.1937188 151.5852836 2018-01-12 04:00:00 night
2018 01 12 00 005 4.899842 59.34659 1 4.0805409 157.7504558 2018-01-12 05:00:00 night
2018 01 12 00 006 4.899842 59.34659 1 4.1558374 133.9815660 2018-01-12 06:00:00 night
2018 01 12 00 007 4.899842 59.34659 1 6.0645056 106.5843607 2018-01-12 07:00:00 night
2018 01 12 00 008 4.899842 59.34659 1 6.2483883 111.4820770 2018-01-12 08:00:00 night
2018 01 12 00 009 4.899842 59.34659 1 7.0188076 119.1272638 2018-01-12 09:00:00 day
2018 01 12 06 004 4.899842 59.34659 1 7.2986656 130.8408949 2018-01-12 10:00:00 day
2018 01 12 06 005 4.899842 59.34659 1 7.6272136 142.3272473 2018-01-12 11:00:00 day
2018 01 12 06 006 4.899842 59.34659 1 8.0355627 146.8408180 2018-01-12 12:00:00 day
2018 01 12 06 007 4.899842 59.34659 1 7.9312555 149.6161815 2018-01-12 13:00:00 day
2018 01 12 06 008 4.899842 59.34659 1 8.2569357 145.5569539 2018-01-12 14:00:00 day
2018 01 12 06 009 4.899842 59.34659 1 8.4306469 141.8830026 2018-01-12 15:00:00 day
2018 01 12 12 004 4.899842 59.34659 1 9.0680354 144.2715519 2018-01-12 16:00:00 day
2018 01 12 12 005 4.899842 59.34659 1 10.9122867 142.8824386 2018-01-12 17:00:00 night
2018 01 12 12 006 4.899842 59.34659 1 11.7544994 145.1951206 2018-01-12 18:00:00 night
2018 01 12 12 007 4.899842 59.34659 1 12.3326364 147.4819731 2018-01-12 19:00:00 night
2018 01 12 12 008 4.899842 59.34659 1 13.1303604 150.0035097 2018-01-12 20:00:00 night
2018 01 12 12 009 4.899842 59.34659 1 13.9225858 152.1279541 2018-01-12 21:00:00 night
2018 01 12 18 004 4.899842 59.34659 1 14.7822259 153.3569756 2018-01-12 22:00:00 night
2018 01 12 18 005 4.899842 59.34659 1 14.8989881 155.9403231 2018-01-12 23:00:00 night
2018 01 12 18 006 4.899842 59.34659 1 14.3222755 156.5588254 2018-01-13 night
2018 01 12 18 007 4.899842 59.34659 1 13.7052564 157.1532736 2018-01-13 01:00:00 night
2018 01 12 18 008 4.899842 59.34659 1 13.5887786 159.0398975 2018-01-13 02:00:00 night
2018 01 12 18 009 4.899842 59.34659 1 13.0835850 165.0890532 2018-01-13 03:00:00 night
2018 01 13 00 004 4.899842 59.34659 1 14.2992668 168.9232741 2018-01-13 04:00:00 night
2018 01 13 00 005 4.899842 59.34659 1 13.9765520 172.5799365 2018-01-13 05:00:00 night
2018 01 13 00 006 4.899842 59.34659 1 14.5194870 169.5741656 2018-01-13 06:00:00 night
2018 01 13 00 007 4.899842 59.34659 1 14.6902426 168.8339045 2018-01-13 07:00:00 night
2018 01 13 00 008 4.899842 59.34659 1 14.9423999 167.5203911 2018-01-13 08:00:00 night
2018 01 13 00 009 4.899842 59.34659 1 15.5376345 163.1671410 2018-01-13 09:00:00 day
2018 01 13 06 004 4.899842 59.34659 1 16.1430725 164.1704230 2018-01-13 10:00:00 day
2018 01 13 06 005 4.899842 59.34659 1 15.8835407 164.4032695 2018-01-13 11:00:00 day
2018 01 13 06 006 4.899842 59.34659 1 16.0509701 170.3344509 2018-01-13 12:00:00 day
2018 01 13 06 007 4.899842 59.34659 1 15.6047205 173.1261154 2018-01-13 13:00:00 day
2018 01 13 06 008 4.899842 59.34659 1 15.3971503 175.9229202 2018-01-13 14:00:00 day
2018 01 13 06 009 4.899842 59.34659 1 15.5170388 177.2883713 2018-01-13 15:00:00 day
2018 01 13 12 004 4.899842 59.34659 1 16.0919009 173.5778754 2018-01-13 16:00:00 day
2018 01 13 12 005 4.899842 59.34659 1 16.0166148 172.2317570 2018-01-13 17:00:00 night
2018 01 13 12 006 4.899842 59.34659 1 16.4060490 171.4357675 2018-01-13 18:00:00 night
2018 01 13 12 007 4.899842 59.34659 1 17.2677723 171.1021674 2018-01-13 19:00:00 night
2018 01 13 12 008 4.899842 59.34659 1 17.5388952 168.9531306 2018-01-13 20:00:00 night
2018 01 13 12 009 4.899842 59.34659 1 18.3152643 166.0207629 2018-01-13 21:00:00 night
2018 01 13 18 004 4.899842 59.34659 1 18.5224672 166.2492298 2018-01-13 22:00:00 night
2018 01 13 18 005 4.899842 59.34659 1 19.1788278 164.6188864 2018-01-13 23:00:00 night
2018 01 13 18 006 4.899842 59.34659 1 20.0413941 164.0268249 2018-01-14 night
2018 01 13 18 007 4.899842 59.34659 1 20.4519680 162.8750449 2018-01-14 01:00:00 night
2018 01 13 18 008 4.899842 59.34659 1 19.7207400 163.2871338 2018-01-14 02:00:00 night
2018 01 13 18 009 4.899842 59.34659 1 19.0357302 162.0800782 2018-01-14 03:00:00 night
2018 01 14 00 004 4.899842 59.34659 1 20.6547545 162.3017892 2018-01-14 04:00:00 night
2018 01 14 00 005 4.899842 59.34659 1 21.0346741 163.7421311 2018-01-14 05:00:00 night
2018 01 14 00 006 4.899842 59.34659 1 21.1105792 162.9507162 2018-01-14 06:00:00 night
2018 01 14 00 007 4.899842 59.34659 1 20.8708363 160.7192016 2018-01-14 07:00:00 night
2018 01 14 00 008 4.899842 59.34659 1 20.7042343 158.8056390 2018-01-14 08:00:00 night
2018 01 14 00 009 4.899842 59.34659 1 20.8954204 159.1832278 2018-01-14 09:00:00 day
2018 01 14 06 004 4.899842 59.34659 1 21.7527648 157.0197806 2018-01-14 10:00:00 day
2018 01 14 06 005 4.899842 59.34659 1 20.5481785 156.5276769 2018-01-14 11:00:00 day
2018 01 14 06 006 4.899842 59.34659 1 20.9967441 151.3475067 2018-01-14 12:00:00 day
2018 01 14 06 007 4.899842 59.34659 1 21.5883991 157.2067985 2018-01-14 13:00:00 day
2018 01 14 06 008 4.899842 59.34659 1 21.8007643 162.9439942 2018-01-14 14:00:00 day
2018 01 14 06 009 4.899842 59.34659 1 22.6415481 161.7799580 2018-01-14 15:00:00 day
2018 01 14 12 004 4.899842 59.34659 1 24.7034220 162.2096464 2018-01-14 16:00:00 day
2018 01 14 12 005 4.899842 59.34659 1 24.1466227 156.9153474 2018-01-14 17:00:00 night
2018 01 14 12 006 4.899842 59.34659 1 25.3162884 153.5509791 2018-01-14 18:00:00 night
2018 01 14 12 007 4.899842 59.34659 1 25.9680278 154.4226620 2018-01-14 19:00:00 night
2018 01 14 12 008 4.899842 59.34659 1 25.7768069 151.5020262 2018-01-14 20:00:00 night
2018 01 14 12 009 4.899842 59.34659 1 25.6793580 149.9983961 2018-01-14 21:00:00 night
2018 01 14 18 004 4.899842 59.34659 1 26.8545381 150.4216226 2018-01-14 22:00:00 night
2018 01 14 18 005 4.899842 59.34659 1 27.2571826 150.0358583 2018-01-14 23:00:00 night
2018 01 14 18 006 4.899842 59.34659 1 26.8744058 147.7611849 2018-01-15 night
2018 01 14 18 007 4.899842 59.34659 1 26.5822299 148.2086684 2018-01-15 01:00:00 night
2018 01 14 18 008 4.899842 59.34659 1 26.4865370 149.3102670 2018-01-15 02:00:00 night
2018 01 14 18 009 4.899842 59.34659 1 26.7993433 151.3677407 2018-01-15 03:00:00 night
2018 01 15 00 004 4.899842 59.34659 1 27.5549620 153.1485386 2018-01-15 04:00:00 night
2018 01 15 00 005 4.899842 59.34659 1 27.6758186 154.1107691 2018-01-15 05:00:00 night
2018 01 15 00 006 4.899842 59.34659 1 27.7312429 154.2135056 2018-01-15 06:00:00 night
2018 01 15 00 007 4.899842 59.34659 1 27.4266206 153.3142323 2018-01-15 07:00:00 night
2018 01 15 00 008 4.899842 59.34659 1 26.8819883 152.3885944 2018-01-15 08:00:00 night
2018 01 15 00 009 4.899842 59.34659 1 26.0614664 151.5042030 2018-01-15 09:00:00 day
2018 01 15 06 004 4.899842 59.34659 1 25.7451435 152.8841960 2018-01-15 10:00:00 day
2018 01 15 06 005 4.899842 59.34659 1 24.6020439 152.7414082 2018-01-15 11:00:00 day
2018 01 15 06 006 4.899842 59.34659 1 24.3158690 153.0937113 2018-01-15 12:00:00 day
2018 01 15 06 007 4.899842 59.34659 1 24.5262434 154.8698733 2018-01-15 13:00:00 day
2018 01 15 06 008 4.899842 59.34659 1 24.1227224 156.8041173 2018-01-15 14:00:00 day
2018 01 15 06 009 4.899842 59.34659 1 24.2532506 159.2050775 2018-01-15 15:00:00 day
2018 01 15 12 004 4.899842 59.34659 1 24.0355225 164.9149796 2018-01-15 16:00:00 day
2018 01 15 12 005 4.899842 59.34659 1 24.2977011 162.9622633 2018-01-15 17:00:00 night
2018 01 15 12 006 4.899842 59.34659 1 24.7136455 160.6172622 2018-01-15 18:00:00 night
2018 01 15 12 007 4.899842 59.34659 1 22.8498138 157.6168556 2018-01-15 19:00:00 night
2018 01 15 12 008 4.899842 59.34659 1 21.2942527 158.7505213 2018-01-15 20:00:00 night
2018 01 15 12 009 4.899842 59.34659 1 17.1026594 154.8321650 2018-01-15 21:00:00 night
2018 01 15 18 004 4.899842 59.34659 1 3.8645975 92.0063371 2018-01-15 22:00:00 night
2018 01 15 18 005 4.899842 59.34659 1 5.7025229 59.4488330 2018-01-15 23:00:00 night
2018 01 15 18 006 4.899842 59.34659 1 6.6099570 39.9390341 2018-01-16 night
2018 01 15 18 007 4.899842 59.34659 1 6.7529740 43.2634081 2018-01-16 01:00:00 night
2018 01 15 18 008 4.899842 59.34659 1 11.3388040 25.7556320 2018-01-16 02:00:00 night
2018 01 15 18 009 4.899842 59.34659 1 8.6797662 32.0408941 2018-01-16 03:00:00 night
2018 01 16 00 004 4.899842 59.34659 1 3.0165664 94.1393452 2018-01-16 04:00:00 night
2018 01 16 00 005 4.899842 59.34659 1 6.6281634 43.6973014 2018-01-16 05:00:00 night
2018 01 16 00 006 4.899842 59.34659 1 6.7633975 40.7305231 2018-01-16 06:00:00 night
2018 01 16 00 007 4.899842 59.34659 1 7.7798292 46.5248904 2018-01-16 07:00:00 night
2018 01 16 00 008 4.899842 59.34659 1 7.5452479 56.1347244 2018-01-16 08:00:00 night
2018 01 16 00 009 4.899842 59.34659 1 7.5865241 64.3266177 2018-01-16 09:00:00 day
2018 01 16 06 004 4.899842 59.34659 1 8.1667262 56.0724345 2018-01-16 10:00:00 day
2018 01 16 06 005 4.899842 59.34659 1 8.4775097 61.1263866 2018-01-16 11:00:00 day
2018 01 16 06 006 4.899842 59.34659 1 7.9514282 65.1347584 2018-01-16 12:00:00 day
2018 01 16 06 007 4.899842 59.34659 1 8.0467727 72.1139886 2018-01-16 13:00:00 day
2018 01 16 06 008 4.899842 59.34659 1 8.8256674 85.7055248 2018-01-16 14:00:00 day
2018 01 16 06 009 4.899842 59.34659 1 11.3198365 97.8730868 2018-01-16 15:00:00 day
2018 01 16 12 004 4.899842 59.34659 1 10.2999849 45.3436127 2018-01-16 16:00:00 day
2018 01 16 12 005 4.899842 59.34659 1 6.8530570 63.5547588 2018-01-16 17:00:00 night
2018 01 16 12 006 4.899842 59.34659 1 6.9819496 85.8894568 2018-01-16 18:00:00 night
2018 01 16 12 007 4.899842 59.34659 1 7.5663943 92.4498528 2018-01-16 19:00:00 night
2018 01 16 12 008 4.899842 59.34659 1 8.4725081 92.1401082 2018-01-16 20:00:00 night
2018 01 16 12 009 4.899842 59.34659 1 6.9406164 97.9977982 2018-01-16 21:00:00 night
2018 01 16 18 004 4.899842 59.34659 1 5.6476654 99.5368373 2018-01-16 22:00:00 night
2018 01 16 18 005 4.899842 59.34659 1 4.4153163 115.6783497 2018-01-16 23:00:00 night
2018 01 16 18 006 4.899842 59.34659 1 5.9431327 165.7961402 2018-01-17 night
2018 01 16 18 007 4.899842 59.34659 1 3.1720176 115.3482604 2018-01-17 01:00:00 night
2018 01 16 18 008 4.899842 59.34659 1 5.2385771 75.4335044 2018-01-17 02:00:00 night
2018 01 16 18 009 4.899842 59.34659 1 3.6469396 39.6068290 2018-01-17 03:00:00 night
2018 01 17 00 004 4.899842 59.34659 1 6.7749053 97.6281394 2018-01-17 04:00:00 night
2018 01 17 00 005 4.899842 59.34659 1 4.0849000 90.4145670 2018-01-17 05:00:00 night
2018 01 17 00 006 4.899842 59.34659 1 4.8754927 114.8906130 2018-01-17 06:00:00 night
2018 01 17 00 007 4.899842 59.34659 1 4.5994860 101.4728726 2018-01-17 07:00:00 night
2018 01 17 00 008 4.899842 59.34659 1 4.9561683 85.6019305 2018-01-17 08:00:00 night
2018 01 17 00 009 4.899842 59.34659 1 7.1947702 89.2587448 2018-01-17 09:00:00 day
2018 01 17 06 004 4.899842 59.34659 1 4.3293586 69.8834777 2018-01-17 10:00:00 day
2018 01 17 06 005 4.899842 59.34659 1 4.8091760 69.2333545 2018-01-17 11:00:00 day
2018 01 17 06 006 4.899842 59.34659 1 4.8177101 69.7849541 2018-01-17 12:00:00 day
2018 01 17 06 007 4.899842 59.34659 1 3.0710462 85.9119493 2018-01-17 13:00:00 day
2018 01 17 06 008 4.899842 59.34659 1 2.8927019 133.2992630 2018-01-17 14:00:00 day
2018 01 17 06 009 4.899842 59.34659 1 4.0820039 140.1896848 2018-01-17 15:00:00 day
2018 01 17 12 004 4.899842 59.34659 1 5.3642858 162.1341474 2018-01-17 16:00:00 day
2018 01 17 12 005 4.899842 59.34659 1 5.9549056 168.0010148 2018-01-17 17:00:00 night
2018 01 17 12 006 4.899842 59.34659 1 6.5001242 144.5211264 2018-01-17 18:00:00 night
2018 01 17 12 007 4.899842 59.34659 1 7.8507475 141.4250976 2018-01-17 19:00:00 night
2018 01 17 12 008 4.899842 59.34659 1 6.3488387 156.6829937 2018-01-17 20:00:00 night
2018 01 17 12 009 4.899842 59.34659 1 11.0960126 136.1838856 2018-01-17 21:00:00 night
2018 01 17 18 004 4.899842 59.34659 1 10.7041879 170.7477212 2018-01-17 22:00:00 night
2018 01 17 18 005 4.899842 59.34659 1 10.3469511 169.2199432 2018-01-17 23:00:00 night
2018 01 17 18 006 4.899842 59.34659 1 10.8963453 156.4013484 2018-01-18 night
2018 01 17 18 007 4.899842 59.34659 1 10.3800201 150.0602312 2018-01-18 01:00:00 night
2018 01 17 18 008 4.899842 59.34659 1 9.1670922 145.5581252 2018-01-18 02:00:00 night
2018 01 17 18 009 4.899842 59.34659 1 10.3250687 168.7422766 2018-01-18 03:00:00 night
2018 01 18 00 004 4.899842 59.34659 1 11.5337482 143.7698873 2018-01-18 04:00:00 night
2018 01 18 00 005 4.899842 59.34659 1 10.1700432 142.2893723 2018-01-18 05:00:00 night
2018 01 18 00 006 4.899842 59.34659 1 8.1125823 145.0523699 2018-01-18 06:00:00 night
2018 01 18 00 007 4.899842 59.34659 1 8.2650469 165.8761188 2018-01-18 07:00:00 night
2018 01 18 00 008 4.899842 59.34659 1 9.1122189 177.7574042 2018-01-18 08:00:00 night
2018 01 18 00 009 4.899842 59.34659 1 8.3509169 -175.7447002 2018-01-18 09:00:00 day
2018 01 18 06 004 4.899842 59.34659 1 8.9338484 159.1837850 2018-01-18 10:00:00 day
2018 01 18 06 005 4.899842 59.34659 1 8.6901667 154.2589049 2018-01-18 11:00:00 day
2018 01 18 06 006 4.899842 59.34659 1 8.3484500 149.5727055 2018-01-18 12:00:00 day
2018 01 18 06 007 4.899842 59.34659 1 7.4101757 138.1294307 2018-01-18 13:00:00 day
2018 01 18 06 008 4.899842 59.34659 1 7.2929291 143.8787878 2018-01-18 14:00:00 day
2018 01 18 06 009 4.899842 59.34659 1 7.4115492 144.3265131 2018-01-18 15:00:00 day
2018 01 18 12 004 4.899842 59.34659 1 9.8071398 156.5782571 2018-01-18 16:00:00 day
2018 01 18 12 005 4.899842 59.34659 1 11.1249590 159.9917091 2018-01-18 17:00:00 night
2018 01 18 12 006 4.899842 59.34659 1 10.8723850 165.3505233 2018-01-18 18:00:00 night
2018 01 18 12 007 4.899842 59.34659 1 10.4210765 165.6570000 2018-01-18 19:00:00 night
2018 01 18 12 008 4.899842 59.34659 1 10.6888709 163.9131554 2018-01-18 20:00:00 night
2018 01 18 12 009 4.899842 59.34659 1 11.1971062 162.2361899 2018-01-18 21:00:00 night
2018 01 18 18 004 4.899842 59.34659 1 12.3906628 146.8087029 2018-01-18 22:00:00 night
2018 01 18 18 005 4.899842 59.34659 1 12.1418129 149.3914178 2018-01-18 23:00:00 night
2018 01 18 18 006 4.899842 59.34659 1 12.8603041 152.0697867 2018-01-19 night
2018 01 18 18 007 4.899842 59.34659 1 16.0274583 141.1666264 2018-01-19 01:00:00 night
2018 01 18 18 008 4.899842 59.34659 1 12.6698279 140.3344286 2018-01-19 02:00:00 night
2018 01 18 18 009 4.899842 59.34659 1 11.1623294 140.5150321 2018-01-19 03:00:00 night
2018 01 19 00 004 4.899842 59.34659 1 7.5031502 127.9355543 2018-01-19 04:00:00 night
2018 01 19 00 005 4.899842 59.34659 1 6.7430507 76.3395987 2018-01-19 05:00:00 night
2018 01 19 00 006 4.899842 59.34659 1 5.6504858 84.7843349 2018-01-19 06:00:00 night
2018 01 19 00 007 4.899842 59.34659 1 6.5512187 76.1455920 2018-01-19 07:00:00 night
2018 01 19 00 008 4.899842 59.34659 1 7.9134808 147.2285902 2018-01-19 08:00:00 night
2018 01 19 00 009 4.899842 59.34659 1 7.2278275 155.3925625 2018-01-19 09:00:00 day
2018 01 19 06 004 4.899842 59.34659 1 7.7028063 114.0643507 2018-01-19 10:00:00 day
2018 01 19 06 005 4.899842 59.34659 1 9.4722830 92.2941035 2018-01-19 11:00:00 day
2018 01 19 06 006 4.899842 59.34659 1 8.5115568 91.3349017 2018-01-19 12:00:00 day
2018 01 19 06 007 4.899842 59.34659 1 7.7175507 93.2678220 2018-01-19 13:00:00 day
2018 01 19 06 008 4.899842 59.34659 1 8.4885287 123.7257170 2018-01-19 14:00:00 day
2018 01 19 06 009 4.899842 59.34659 1 6.2797672 92.2447879 2018-01-19 15:00:00 day
2018 01 19 12 004 4.899842 59.34659 1 8.2441944 126.2073284 2018-01-19 16:00:00 day
2018 01 19 12 005 4.899842 59.34659 1 9.3041290 139.3502426 2018-01-19 17:00:00 night
2018 01 19 12 006 4.899842 59.34659 1 8.1042349 112.6478467 2018-01-19 18:00:00 night
2018 01 19 12 007 4.899842 59.34659 1 6.9567883 119.0089895 2018-01-19 19:00:00 night
2018 01 19 12 008 4.899842 59.34659 1 5.2673541 67.1092071 2018-01-19 20:00:00 night
2018 01 19 12 009 4.899842 59.34659 1 4.0655429 17.4662087 2018-01-19 21:00:00 night
2018 01 19 18 004 4.899842 59.34659 1 6.6367468 120.7855985 2018-01-19 22:00:00 night
2018 01 19 18 005 4.899842 59.34659 1 5.7261596 115.5552043 2018-01-19 23:00:00 night
2018 01 19 18 006 4.899842 59.34659 1 5.8734475 147.2751482 2018-01-20 night
2018 01 19 18 007 4.899842 59.34659 1 6.1253971 134.4366345 2018-01-20 01:00:00 night
2018 01 19 18 008 4.899842 59.34659 1 6.5147374 160.5238843 2018-01-20 02:00:00 night
2018 01 19 18 009 4.899842 59.34659 1 3.0962265 77.0633940 2018-01-20 03:00:00 night
2018 01 20 00 004 4.899842 59.34659 1 9.4409412 159.7727075 2018-01-20 04:00:00 night
2018 01 20 00 005 4.899842 59.34659 1 8.7151820 166.8076725 2018-01-20 05:00:00 night
2018 01 20 00 006 4.899842 59.34659 1 8.0028583 -179.3445217 2018-01-20 06:00:00 night
2018 01 20 00 007 4.899842 59.34659 1 8.9156312 176.6393322 2018-01-20 07:00:00 night
2018 01 20 00 008 4.899842 59.34659 1 11.0772410 173.1512993 2018-01-20 08:00:00 night
2018 01 20 00 009 4.899842 59.34659 1 9.1993091 174.5115577 2018-01-20 09:00:00 day
2018 01 20 06 004 4.899842 59.34659 1 9.7148796 178.8361397 2018-01-20 10:00:00 day
2018 01 20 06 005 4.899842 59.34659 1 9.8989307 175.9433852 2018-01-20 11:00:00 day
2018 01 20 06 006 4.899842 59.34659 1 10.0851140 167.1553541 2018-01-20 12:00:00 day
2018 01 20 06 007 4.899842 59.34659 1 9.5380578 167.6486173 2018-01-20 13:00:00 day
2018 01 20 06 008 4.899842 59.34659 1 9.1067954 153.3204033 2018-01-20 14:00:00 day
2018 01 20 06 009 4.899842 59.34659 1 4.3611203 113.7079154 2018-01-20 15:00:00 day
2018 01 20 12 004 4.899842 59.34659 1 7.5267865 -144.5043558 2018-01-20 16:00:00 day
2018 01 20 12 005 4.899842 59.34659 1 7.1926017 -142.8058769 2018-01-20 17:00:00 night
2018 01 20 12 006 4.899842 59.34659 1 6.9531536 -142.6579318 2018-01-20 18:00:00 night
2018 01 20 12 007 4.899842 59.34659 1 8.2922497 -155.5460195 2018-01-20 19:00:00 night
2018 01 20 12 008 4.899842 59.34659 1 7.6303832 -163.2326263 2018-01-20 20:00:00 night
2018 01 20 12 009 4.899842 59.34659 1 6.7898170 -156.8921841 2018-01-20 21:00:00 night
2018 01 20 18 004 4.899842 59.34659 1 5.8250746 -153.7208169 2018-01-20 22:00:00 night
2018 01 20 18 005 4.899842 59.34659 1 5.1639130 -153.9784353 2018-01-20 23:00:00 night
2018 01 20 18 006 4.899842 59.34659 1 4.9493690 -155.5670825 2018-01-21 night
2018 01 20 18 007 4.899842 59.34659 1 4.5220850 -155.2963438 2018-01-21 01:00:00 night
2018 01 20 18 008 4.899842 59.34659 1 5.9795480 -176.6027425 2018-01-21 02:00:00 night
2018 01 20 18 009 4.899842 59.34659 1 5.0631831 163.2475668 2018-01-21 03:00:00 night
2018 01 21 00 004 4.899842 59.34659 1 5.7810108 178.3021910 2018-01-21 04:00:00 night
2018 01 21 00 005 4.899842 59.34659 1 5.9259751 168.8080861 2018-01-21 05:00:00 night
2018 01 21 00 006 4.899842 59.34659 1 6.2825524 163.3249743 2018-01-21 06:00:00 night
2018 01 21 00 007 4.899842 59.34659 1 6.2102075 158.8270812 2018-01-21 07:00:00 night
2018 01 21 00 008 4.899842 59.34659 1 8.3001472 160.0546625 2018-01-21 08:00:00 day
2018 01 21 00 009 4.899842 59.34659 1 8.0196236 172.0872644 2018-01-21 09:00:00 day
2018 01 21 06 004 4.899842 59.34659 1 9.3556570 151.4915426 2018-01-21 10:00:00 day
2018 01 21 06 005 4.899842 59.34659 1 10.0442697 149.1263007 2018-01-21 11:00:00 day
2018 01 21 06 006 4.899842 59.34659 1 11.2472281 147.6845702 2018-01-21 12:00:00 day
2018 01 21 06 007 4.899842 59.34659 1 11.4127721 148.5149542 2018-01-21 13:00:00 day
2018 01 21 06 008 4.899842 59.34659 1 11.5327202 152.9029502 2018-01-21 14:00:00 day
2018 01 21 06 009 4.899842 59.34659 1 12.1368241 157.6558491 2018-01-21 15:00:00 day
2018 01 21 12 004 4.899842 59.34659 1 14.0676807 159.3190279 2018-01-21 16:00:00 day
2018 01 21 12 005 4.899842 59.34659 1 14.4513103 165.4177612 2018-01-21 17:00:00 night
2018 01 21 12 006 4.899842 59.34659 1 14.5191306 170.6140899 2018-01-21 18:00:00 night
2018 01 21 12 007 4.899842 59.34659 1 14.6659116 169.5851572 2018-01-21 19:00:00 night
2018 01 21 12 008 4.899842 59.34659 1 14.8969055 169.4362210 2018-01-21 20:00:00 night
2018 01 21 12 009 4.899842 59.34659 1 15.6935513 172.1861371 2018-01-21 21:00:00 night
2018 01 21 18 004 4.899842 59.34659 1 15.8626848 170.8442051 2018-01-21 22:00:00 night
2018 01 21 18 005 4.899842 59.34659 1 16.3076123 170.1204489 2018-01-21 23:00:00 night
2018 01 21 18 006 4.899842 59.34659 1 17.1956250 171.6839018 2018-01-22 night
2018 01 21 18 007 4.899842 59.34659 1 18.5952136 -179.1928573 2018-01-22 01:00:00 night
2018 01 21 18 008 4.899842 59.34659 1 17.5448270 177.9033203 2018-01-22 02:00:00 night
2018 01 21 18 009 4.899842 59.34659 1 15.5814897 175.9535798 2018-01-22 03:00:00 night
2018 01 22 00 004 4.899842 59.34659 1 15.6523485 172.3271662 2018-01-22 04:00:00 night
2018 01 22 00 005 4.899842 59.34659 1 14.6943650 177.7921311 2018-01-22 05:00:00 night
2018 01 22 00 006 4.899842 59.34659 1 18.2035027 172.0038987 2018-01-22 06:00:00 night
2018 01 22 00 007 4.899842 59.34659 1 18.1757340 167.9238713 2018-01-22 07:00:00 night
2018 01 22 00 008 4.899842 59.34659 1 18.1971452 171.5840199 2018-01-22 08:00:00 day
2018 01 22 00 009 4.899842 59.34659 1 17.8802601 172.3487509 2018-01-22 09:00:00 day
2018 01 22 06 004 4.899842 59.34659 1 17.6378864 173.7640624 2018-01-22 10:00:00 day
2018 01 22 06 005 4.899842 59.34659 1 17.9492739 172.4962683 2018-01-22 11:00:00 day
2018 01 22 06 006 4.899842 59.34659 1 18.2317323 169.4550483 2018-01-22 12:00:00 day
2018 01 22 06 007 4.899842 59.34659 1 18.9822495 168.4539752 2018-01-22 13:00:00 day
2018 01 22 06 008 4.899842 59.34659 1 19.2516154 165.5024348 2018-01-22 14:00:00 day
2018 01 22 06 009 4.899842 59.34659 1 19.4372700 161.8796204 2018-01-22 15:00:00 day
2018 01 22 12 004 4.899842 59.34659 1 19.7926073 162.1542532 2018-01-22 16:00:00 day
2018 01 22 12 005 4.899842 59.34659 1 20.1220329 163.5712216 2018-01-22 17:00:00 night
2018 01 22 12 006 4.899842 59.34659 1 20.1519232 163.4238513 2018-01-22 18:00:00 night
2018 01 22 12 007 4.899842 59.34659 1 19.9834655 163.1143190 2018-01-22 19:00:00 night
2018 01 22 12 008 4.899842 59.34659 1 20.1247147 163.2304969 2018-01-22 20:00:00 night
2018 01 22 12 009 4.899842 59.34659 1 20.1244056 160.6701020 2018-01-22 21:00:00 night
2018 01 22 18 004 4.899842 59.34659 1 21.0327507 157.9500765 2018-01-22 22:00:00 night
2018 01 22 18 005 4.899842 59.34659 1 21.0412062 159.2038691 2018-01-22 23:00:00 night
2018 01 22 18 006 4.899842 59.34659 1 21.3018758 157.5640556 2018-01-23 night
2018 01 22 18 007 4.899842 59.34659 1 20.8592561 152.8543480 2018-01-23 01:00:00 night
2018 01 22 18 008 4.899842 59.34659 1 20.4674129 149.4399359 2018-01-23 02:00:00 night
2018 01 22 18 009 4.899842 59.34659 1 19.2640704 144.8258826 2018-01-23 03:00:00 night
2018 01 23 00 004 4.899842 59.34659 1 19.1530160 148.7320822 2018-01-23 04:00:00 night
2018 01 23 00 005 4.899842 59.34659 1 17.8516558 146.8687147 2018-01-23 05:00:00 night
2018 01 23 00 006 4.899842 59.34659 1 16.2435797 145.0494359 2018-01-23 06:00:00 night
2018 01 23 00 007 4.899842 59.34659 1 13.7143291 137.4322184 2018-01-23 07:00:00 night
2018 01 23 00 008 4.899842 59.34659 1 14.2358774 150.6502318 2018-01-23 08:00:00 day
2018 01 23 00 009 4.899842 59.34659 1 15.8535494 147.2481437 2018-01-23 09:00:00 day
2018 01 23 06 004 4.899842 59.34659 1 16.9522517 138.2512481 2018-01-23 10:00:00 day
2018 01 23 06 005 4.899842 59.34659 1 17.6251595 137.2614708 2018-01-23 11:00:00 day
2018 01 23 06 006 4.899842 59.34659 1 17.6058162 140.1258259 2018-01-23 12:00:00 day
2018 01 23 06 007 4.899842 59.34659 1 17.6556132 139.5379686 2018-01-23 13:00:00 day
2018 01 23 06 008 4.899842 59.34659 1 20.1234845 142.0828812 2018-01-23 14:00:00 day
2018 01 23 06 009 4.899842 59.34659 1 20.7354618 151.3844189 2018-01-23 15:00:00 day
2018 01 23 12 004 4.899842 59.34659 1 21.0556052 146.1527383 2018-01-23 16:00:00 day
2018 01 23 12 005 4.899842 59.34659 1 19.6707144 146.4512719 2018-01-23 17:00:00 night
2018 01 23 12 006 4.899842 59.34659 1 16.5721695 133.2502735 2018-01-23 18:00:00 night
2018 01 23 12 007 4.899842 59.34659 1 13.9268232 83.4954200 2018-01-23 19:00:00 night
2018 01 23 12 008 4.899842 59.34659 1 13.2081243 72.7503224 2018-01-23 20:00:00 night
2018 01 23 12 009 4.899842 59.34659 1 11.3691179 67.6400340 2018-01-23 21:00:00 night
2018 01 23 18 004 4.899842 59.34659 1 10.2303996 67.3864326 2018-01-23 22:00:00 night
2018 01 23 18 005 4.899842 59.34659 1 7.0480805 78.1034649 2018-01-23 23:00:00 night
2018 01 23 18 006 4.899842 59.34659 1 8.4452207 100.6383580 2018-01-24 night
2018 01 23 18 007 4.899842 59.34659 1 12.1714967 67.0763696 2018-01-24 01:00:00 night
2018 01 23 18 008 4.899842 59.34659 1 14.1476231 68.4716439 2018-01-24 02:00:00 night
2018 01 23 18 009 4.899842 59.34659 1 12.6565901 58.1227523 2018-01-24 03:00:00 night
2018 01 24 00 004 4.899842 59.34659 1 11.5900349 71.4489546 2018-01-24 04:00:00 night
2018 01 24 00 005 4.899842 59.34659 1 9.3960892 88.0404511 2018-01-24 05:00:00 night
2018 01 24 00 006 4.899842 59.34659 1 8.5546248 125.0690795 2018-01-24 06:00:00 night
2018 01 24 00 007 4.899842 59.34659 1 13.1053917 143.9865607 2018-01-24 07:00:00 night
2018 01 24 00 008 4.899842 59.34659 1 15.2846992 157.5647594 2018-01-24 08:00:00 day
2018 01 24 00 009 4.899842 59.34659 1 18.2382127 170.3434993 2018-01-24 09:00:00 day
2018 01 24 06 004 4.899842 59.34659 1 21.5697437 154.8240240 2018-01-24 10:00:00 day
2018 01 24 06 005 4.899842 59.34659 1 22.6861315 119.1867387 2018-01-24 11:00:00 day
2018 01 24 06 006 4.899842 59.34659 1 21.3313840 111.2931983 2018-01-24 12:00:00 day
2018 01 24 06 007 4.899842 59.34659 1 19.5947064 98.6431819 2018-01-24 13:00:00 day
2018 01 24 06 008 4.899842 59.34659 1 19.3317822 98.1500376 2018-01-24 14:00:00 day
2018 01 24 06 009 4.899842 59.34659 1 20.5625199 102.1282268 2018-01-24 15:00:00 day
2018 01 24 12 004 4.899842 59.34659 1 23.3965941 106.1557066 2018-01-24 16:00:00 day
2018 01 24 12 005 4.899842 59.34659 1 25.8407679 99.3874623 2018-01-24 17:00:00 night
2018 01 24 12 006 4.899842 59.34659 1 24.7007689 93.0642986 2018-01-24 18:00:00 night
2018 01 24 12 007 4.899842 59.34659 1 24.0338840 93.5951020 2018-01-24 19:00:00 night
2018 01 24 12 008 4.899842 59.34659 1 25.2488901 88.5826541 2018-01-24 20:00:00 night
2018 01 24 12 009 4.899842 59.34659 1 20.8363437 66.4832103 2018-01-24 21:00:00 night
2018 01 24 18 004 4.899842 59.34659 1 22.5942527 53.0239180 2018-01-24 22:00:00 night
2018 01 24 18 005 4.899842 59.34659 1 22.8741230 48.3984424 2018-01-24 23:00:00 night
2018 01 24 18 006 4.899842 59.34659 1 21.4183266 46.8549191 2018-01-25 night
2018 01 24 18 007 4.899842 59.34659 1 17.5891519 41.4894393 2018-01-25 01:00:00 night
2018 01 24 18 008 4.899842 59.34659 1 15.5633599 43.1513242 2018-01-25 02:00:00 night
2018 01 24 18 009 4.899842 59.34659 1 12.6445637 45.9111605 2018-01-25 03:00:00 night
2018 01 25 00 004 4.899842 59.34659 1 10.3874280 54.4524110 2018-01-25 04:00:00 night
2018 01 25 00 005 4.899842 59.34659 1 9.4548369 61.7732065 2018-01-25 05:00:00 night
2018 01 25 00 006 4.899842 59.34659 1 9.4210384 65.4828384 2018-01-25 06:00:00 night
2018 01 25 00 007 4.899842 59.34659 1 9.3721344 68.4210265 2018-01-25 07:00:00 night
2018 01 25 00 008 4.899842 59.34659 1 9.1565716 72.7570155 2018-01-25 08:00:00 day
2018 01 25 00 009 4.899842 59.34659 1 9.3846056 79.6993031 2018-01-25 09:00:00 day
2018 01 25 06 004 4.899842 59.34659 1 8.4102995 87.3210225 2018-01-25 10:00:00 day
2018 01 25 06 005 4.899842 59.34659 1 7.9323577 82.2103921 2018-01-25 11:00:00 day
2018 01 25 06 006 4.899842 59.34659 1 8.2410576 80.2945914 2018-01-25 12:00:00 day
2018 01 25 06 007 4.899842 59.34659 1 7.0873820 79.9424857 2018-01-25 13:00:00 day
2018 01 25 06 008 4.899842 59.34659 1 6.5912416 76.2324735 2018-01-25 14:00:00 day
2018 01 25 06 009 4.899842 59.34659 1 9.3876496 92.8743341 2018-01-25 15:00:00 day
2018 01 25 12 004 4.899842 59.34659 1 6.2028210 96.3590097 2018-01-25 16:00:00 day
2018 01 25 12 005 4.899842 59.34659 1 6.7513194 94.1738281 2018-01-25 17:00:00 night
2018 01 25 12 006 4.899842 59.34659 1 7.0907535 90.5397995 2018-01-25 18:00:00 night
2018 01 25 12 007 4.899842 59.34659 1 7.7056774 90.7413497 2018-01-25 19:00:00 night
2018 01 25 12 008 4.899842 59.34659 1 7.9015129 82.9595363 2018-01-25 20:00:00 night
2018 01 25 12 009 4.899842 59.34659 1 7.3355770 76.2068256 2018-01-25 21:00:00 night
2018 01 25 18 004 4.899842 59.34659 1 5.9031912 74.0280766 2018-01-25 22:00:00 night
2018 01 25 18 005 4.899842 59.34659 1 6.1117897 66.3446821 2018-01-25 23:00:00 night
2018 01 25 18 006 4.899842 59.34659 1 6.2296499 62.5283823 2018-01-26 night
2018 01 25 18 007 4.899842 59.34659 1 7.5597494 -31.5396867 2018-01-26 01:00:00 night
2018 01 25 18 008 4.899842 59.34659 1 3.2583950 -57.2796214 2018-01-26 02:00:00 night
2018 01 25 18 009 4.899842 59.34659 1 2.2389933 11.5174589 2018-01-26 03:00:00 night
2018 01 26 00 004 4.899842 59.34659 1 6.6406286 -13.3666086 2018-01-26 04:00:00 night
2018 01 26 00 005 4.899842 59.34659 1 7.4509919 -29.7761425 2018-01-26 05:00:00 night
2018 01 26 00 006 4.899842 59.34659 1 7.2473488 -35.2278193 2018-01-26 06:00:00 night
2018 01 26 00 007 4.899842 59.34659 1 6.4260830 -37.4417768 2018-01-26 07:00:00 night
2018 01 26 00 008 4.899842 59.34659 1 5.8895476 -28.9596897 2018-01-26 08:00:00 day
2018 01 26 00 009 4.899842 59.34659 1 8.1971167 -24.1345108 2018-01-26 09:00:00 day
2018 01 26 06 004 4.899842 59.34659 1 8.3717131 -19.7546015 2018-01-26 10:00:00 day
2018 01 26 06 005 4.899842 59.34659 1 8.1803733 -19.9341562 2018-01-26 11:00:00 day
2018 01 26 06 006 4.899842 59.34659 1 7.7319295 -17.5353860 2018-01-26 12:00:00 day
2018 01 26 06 007 4.899842 59.34659 1 7.0327946 -16.2407374 2018-01-26 13:00:00 day
2018 01 26 06 008 4.899842 59.34659 1 6.9135553 -16.4587773 2018-01-26 14:00:00 day
2018 01 26 06 009 4.899842 59.34659 1 6.2146848 -15.0454058 2018-01-26 15:00:00 day
2018 01 26 12 004 4.899842 59.34659 1 5.4335575 -5.6002185 2018-01-26 16:00:00 day
2018 01 26 12 005 4.899842 59.34659 1 5.6139554 -6.5822481 2018-01-26 17:00:00 night
2018 01 26 12 006 4.899842 59.34659 1 5.4413969 -11.2978957 2018-01-26 18:00:00 night
2018 01 26 12 007 4.899842 59.34659 1 5.2412836 -10.6191839 2018-01-26 19:00:00 night
2018 01 26 12 008 4.899842 59.34659 1 4.2311357 -6.5332101 2018-01-26 20:00:00 night
2018 01 26 12 009 4.899842 59.34659 1 2.9046640 -0.4177728 2018-01-26 21:00:00 night
2018 01 26 18 004 4.899842 59.34659 1 2.3031492 14.2205586 2018-01-26 22:00:00 night
2018 01 26 18 005 4.899842 59.34659 1 2.5140192 43.6562774 2018-01-26 23:00:00 night
2018 01 26 18 006 4.899842 59.34659 1 3.0485045 62.4738967 2018-01-27 night
2018 01 26 18 007 4.899842 59.34659 1 3.2829103 77.6785302 2018-01-27 01:00:00 night
2018 01 26 18 008 4.899842 59.34659 1 3.3320312 92.7087888 2018-01-27 02:00:00 night
2018 01 26 18 009 4.899842 59.34659 1 3.8303239 110.4336129 2018-01-27 03:00:00 night
2018 01 27 00 004 4.899842 59.34659 1 5.1648949 146.1361337 2018-01-27 04:00:00 night
2018 01 27 00 005 4.899842 59.34659 1 7.2482090 163.5339096 2018-01-27 05:00:00 night
2018 01 27 00 006 4.899842 59.34659 1 9.1851021 152.8549201 2018-01-27 06:00:00 night
2018 01 27 00 007 4.899842 59.34659 1 10.3150381 140.0814385 2018-01-27 07:00:00 night
2018 01 27 00 008 4.899842 59.34659 1 12.8612610 148.3742125 2018-01-27 08:00:00 day
2018 01 27 00 009 4.899842 59.34659 1 15.1069747 154.0429814 2018-01-27 09:00:00 day
2018 01 27 06 004 4.899842 59.34659 1 17.3057943 156.6898752 2018-01-27 10:00:00 day
2018 01 27 06 005 4.899842 59.34659 1 19.1016812 156.9900502 2018-01-27 11:00:00 day
2018 01 27 06 006 4.899842 59.34659 1 20.0309711 162.5806386 2018-01-27 12:00:00 day
2018 01 27 06 007 4.899842 59.34659 1 21.8302077 154.4140204 2018-01-27 13:00:00 day
2018 01 27 06 008 4.899842 59.34659 1 23.4950827 150.3573544 2018-01-27 14:00:00 day
2018 01 27 06 009 4.899842 59.34659 1 24.5023032 144.3643216 2018-01-27 15:00:00 day
2018 01 27 12 004 4.899842 59.34659 1 23.8315942 146.0718662 2018-01-27 16:00:00 day
2018 01 27 12 005 4.899842 59.34659 1 21.4963253 150.3778727 2018-01-27 17:00:00 night
2018 01 27 12 006 4.899842 59.34659 1 14.8336960 67.5882174 2018-01-27 18:00:00 night
2018 01 27 12 007 4.899842 59.34659 1 13.8805333 64.6284729 2018-01-27 19:00:00 night
2018 01 27 12 008 4.899842 59.34659 1 10.9109228 72.4885028 2018-01-27 20:00:00 night
2018 01 27 12 009 4.899842 59.34659 1 12.8884210 72.3742657 2018-01-27 21:00:00 night
2018 01 27 18 004 4.899842 59.34659 1 13.1769255 83.3031753 2018-01-27 22:00:00 night
2018 01 27 18 005 4.899842 59.34659 1 17.1594634 75.7939829 2018-01-27 23:00:00 night
2018 01 27 18 006 4.899842 59.34659 1 17.0041495 86.2872620 2018-01-28 night
2018 01 27 18 007 4.899842 59.34659 1 20.0316479 68.3023182 2018-01-28 01:00:00 night
2018 01 27 18 008 4.899842 59.34659 1 20.5304548 66.2561495 2018-01-28 02:00:00 night
2018 01 27 18 009 4.899842 59.34659 1 18.4324806 51.0751755 2018-01-28 03:00:00 night
2018 01 28 00 004 4.899842 59.34659 1 20.1836332 44.3028433 2018-01-28 04:00:00 night
2018 01 28 00 005 4.899842 59.34659 1 22.0253768 35.9643084 2018-01-28 05:00:00 night
2018 01 28 00 006 4.899842 59.34659 1 20.0802583 30.2878395 2018-01-28 06:00:00 night
2018 01 28 00 007 4.899842 59.34659 1 21.3609452 26.2479575 2018-01-28 07:00:00 night
2018 01 28 00 008 4.899842 59.34659 1 20.3871902 27.8952109 2018-01-28 08:00:00 day
2018 01 28 00 009 4.899842 59.34659 1 18.4918332 30.6081463 2018-01-28 09:00:00 day
2018 01 28 06 004 4.899842 59.34659 1 19.8210545 31.3657977 2018-01-28 10:00:00 day
2018 01 28 06 005 4.899842 59.34659 1 18.9524321 32.5667622 2018-01-28 11:00:00 day
2018 01 28 06 006 4.899842 59.34659 1 17.7205575 35.8176537 2018-01-28 12:00:00 day
2018 01 28 06 007 4.899842 59.34659 1 17.4856014 36.6447362 2018-01-28 13:00:00 day
2018 01 28 06 008 4.899842 59.34659 1 16.8771767 31.8087050 2018-01-28 14:00:00 day
2018 01 28 06 009 4.899842 59.34659 1 15.4540102 29.0666297 2018-01-28 15:00:00 day
2018 01 28 12 004 4.899842 59.34659 1 14.0014410 38.5866831 2018-01-28 16:00:00 day
2018 01 28 12 005 4.899842 59.34659 1 12.6631745 38.0738659 2018-01-28 17:00:00 night
2018 01 28 12 006 4.899842 59.34659 1 11.5296130 38.2497770 2018-01-28 18:00:00 night
2018 01 28 12 007 4.899842 59.34659 1 9.9806951 41.0191743 2018-01-28 19:00:00 night
2018 01 28 12 008 4.899842 59.34659 1 9.2245936 50.4697309 2018-01-28 20:00:00 night
2018 01 28 12 009 4.899842 59.34659 1 8.4669090 54.5066865 2018-01-28 21:00:00 night
2018 01 28 18 004 4.899842 59.34659 1 8.4106176 62.2474045 2018-01-28 22:00:00 night
2018 01 28 18 005 4.899842 59.34659 1 7.7008238 66.8281676 2018-01-28 23:00:00 night
2018 01 28 18 006 4.899842 59.34659 1 6.0160924 66.4924010 2018-01-29 night
2018 01 28 18 007 4.899842 59.34659 1 7.7102102 94.2283116 2018-01-29 01:00:00 night
2018 01 28 18 008 4.899842 59.34659 1 3.2594727 142.7707354 2018-01-29 02:00:00 night
2018 01 28 18 009 4.899842 59.34659 1 4.9193290 175.7509034 2018-01-29 03:00:00 night
2018 01 29 00 004 4.899842 59.34659 1 3.5698480 169.7516239 2018-01-29 04:00:00 night
2018 01 29 00 005 4.899842 59.34659 1 2.9898434 132.4224119 2018-01-29 05:00:00 night
2018 01 29 00 006 4.899842 59.34659 1 3.6122234 65.9499280 2018-01-29 06:00:00 night
2018 01 29 00 007 4.899842 59.34659 1 8.9344314 27.5043870 2018-01-29 07:00:00 night
2018 01 29 00 008 4.899842 59.34659 1 9.7710512 27.1541137 2018-01-29 08:00:00 day
2018 01 29 00 009 4.899842 59.34659 1 10.0817273 35.7621661 2018-01-29 09:00:00 day
2018 01 29 06 004 4.899842 59.34659 1 13.2283604 26.2923036 2018-01-29 10:00:00 day
2018 01 29 06 005 4.899842 59.34659 1 13.2546648 27.1984694 2018-01-29 11:00:00 day
2018 01 29 06 006 4.899842 59.34659 1 15.2466165 20.3397524 2018-01-29 12:00:00 day
2018 01 29 06 007 4.899842 59.34659 1 17.1915392 2.7085950 2018-01-29 13:00:00 day
2018 01 29 06 008 4.899842 59.34659 1 13.3891050 -3.4975688 2018-01-29 14:00:00 day
2018 01 29 06 009 4.899842 59.34659 1 14.2573594 -11.9135948 2018-01-29 15:00:00 day
2018 01 29 12 004 4.899842 59.34659 1 14.2753749 13.4085638 2018-01-29 16:00:00 day
2018 01 29 12 005 4.899842 59.34659 1 14.6860752 11.7334989 2018-01-29 17:00:00 night
2018 01 29 12 006 4.899842 59.34659 1 12.6907010 4.1794816 2018-01-29 18:00:00 night
2018 01 29 12 007 4.899842 59.34659 1 19.2253210 13.4623727 2018-01-29 19:00:00 night
2018 01 29 12 008 4.899842 59.34659 1 12.3058515 27.7378734 2018-01-29 20:00:00 night
2018 01 29 12 009 4.899842 59.34659 1 11.1911289 -12.8467495 2018-01-29 21:00:00 night
2018 01 29 18 004 4.899842 59.34659 1 13.6002162 9.8669139 2018-01-29 22:00:00 night
2018 01 29 18 005 4.899842 59.34659 1 15.1860840 6.3444004 2018-01-29 23:00:00 night
2018 01 29 18 006 4.899842 59.34659 1 15.0134375 4.2618028 2018-01-30 night
2018 01 29 18 007 4.899842 59.34659 1 15.2255134 10.4932775 2018-01-30 01:00:00 night
2018 01 29 18 008 4.899842 59.34659 1 13.3457023 14.8194487 2018-01-30 02:00:00 night
2018 01 29 18 009 4.899842 59.34659 1 12.7260404 17.2508803 2018-01-30 03:00:00 night
2018 01 30 00 004 4.899842 59.34659 1 10.9430412 23.5083516 2018-01-30 04:00:00 night
2018 01 30 00 005 4.899842 59.34659 1 11.1119667 28.6073829 2018-01-30 05:00:00 night
2018 01 30 00 006 4.899842 59.34659 1 11.7577002 32.6972958 2018-01-30 06:00:00 night
2018 01 30 00 007 4.899842 59.34659 1 12.2163235 33.9070929 2018-01-30 07:00:00 night
2018 01 30 00 008 4.899842 59.34659 1 11.0398346 36.7745349 2018-01-30 08:00:00 day
2018 01 30 00 009 4.899842 59.34659 1 7.3595933 51.3327152 2018-01-30 09:00:00 day
2018 01 30 06 004 4.899842 59.34659 1 9.0182329 56.7289771 2018-01-30 10:00:00 day
2018 01 30 06 005 4.899842 59.34659 1 9.2010398 66.6998634 2018-01-30 11:00:00 day
2018 01 30 06 006 4.899842 59.34659 1 8.4225044 85.3010616 2018-01-30 12:00:00 day
2018 01 30 06 007 4.899842 59.34659 1 13.8529138 83.9016746 2018-01-30 13:00:00 day
2018 01 30 06 008 4.899842 59.34659 1 12.2072715 95.4085511 2018-01-30 14:00:00 day
2018 01 30 06 009 4.899842 59.34659 1 14.2777318 64.0856912 2018-01-30 15:00:00 day
2018 01 30 12 004 4.899842 59.34659 1 12.7814244 75.1134194 2018-01-30 16:00:00 day
2018 01 30 12 005 4.899842 59.34659 1 12.0669234 74.3120918 2018-01-30 17:00:00 night
2018 01 30 12 006 4.899842 59.34659 1 13.1070896 74.5164101 2018-01-30 18:00:00 night
2018 01 30 12 007 4.899842 59.34659 1 13.3919368 71.7505815 2018-01-30 19:00:00 night
2018 01 30 12 008 4.899842 59.34659 1 11.1253200 94.0413524 2018-01-30 20:00:00 night
2018 01 30 12 009 4.899842 59.34659 1 12.0190175 103.7781569 2018-01-30 21:00:00 night
2018 01 30 18 004 4.899842 59.34659 1 14.5355985 105.3285124 2018-01-30 22:00:00 night
2018 01 30 18 005 4.899842 59.34659 1 14.9985192 108.5082750 2018-01-30 23:00:00 night
2018 01 30 18 006 4.899842 59.34659 1 15.5342149 108.6641071 2018-01-31 night
2018 01 30 18 007 4.899842 59.34659 1 15.4842529 104.8910501 2018-01-31 01:00:00 night
2018 01 30 18 008 4.899842 59.34659 1 15.7418564 112.3857991 2018-01-31 02:00:00 night
2018 01 30 18 009 4.899842 59.34659 1 16.0881100 121.0316034 2018-01-31 03:00:00 night
2018 01 31 00 004 4.899842 59.34659 1 14.8705397 125.7751532 2018-01-31 04:00:00 night
2018 01 31 00 005 4.899842 59.34659 1 14.5846657 123.3265154 2018-01-31 05:00:00 night
2018 01 31 00 006 4.899842 59.34659 1 14.1807899 119.5347898 2018-01-31 06:00:00 night
2018 01 31 00 007 4.899842 59.34659 1 12.8809280 121.7407326 2018-01-31 07:00:00 night
2018 01 31 00 008 4.899842 59.34659 1 12.4730525 115.9935691 2018-01-31 08:00:00 day
2018 01 31 00 009 4.899842 59.34659 1 12.1304744 113.4228756 2018-01-31 09:00:00 day
2018 01 31 06 004 4.899842 59.34659 1 9.4694410 117.7136616 2018-01-31 10:00:00 day
2018 01 31 06 005 4.899842 59.34659 1 8.1549583 97.6770633 2018-01-31 11:00:00 day
2018 01 31 06 006 4.899842 59.34659 1 5.5860954 59.2134733 2018-01-31 12:00:00 day
2018 01 31 06 007 4.899842 59.34659 1 6.2798789 59.5533932 2018-01-31 13:00:00 day
2018 01 31 06 008 4.899842 59.34659 1 5.7034571 101.6177989 2018-01-31 14:00:00 day
2018 01 31 06 009 4.899842 59.34659 1 8.6126216 64.2852564 2018-01-31 15:00:00 day
2018 01 31 12 004 4.899842 59.34659 1 9.2665738 72.5308502 2018-01-31 16:00:00 day
2018 01 31 12 005 4.899842 59.34659 1 8.8023354 70.8819353 2018-01-31 17:00:00 night
2018 01 31 12 006 4.899842 59.34659 1 8.0099798 81.1108250 2018-01-31 18:00:00 night
2018 01 31 12 007 4.899842 59.34659 1 9.0374472 93.0484904 2018-01-31 19:00:00 night
2018 01 31 12 008 4.899842 59.34659 1 8.4081310 116.6073202 2018-01-31 20:00:00 night
2018 01 31 12 009 4.899842 59.34659 1 7.6869268 129.5785468 2018-01-31 21:00:00 night
2018 01 31 18 004 4.899842 59.34659 1 7.6348544 142.9447568 2018-01-31 22:00:00 night
2018 01 31 18 005 4.899842 59.34659 1 5.2573967 141.2016131 2018-01-31 23:00:00 night
2018 01 31 18 006 4.899842 59.34659 1 6.0571270 175.8738969 2018-02-01 night
2018 01 31 18 007 4.899842 59.34659 1 9.7014436 166.4958404 2018-02-01 01:00:00 night
2018 01 31 18 008 4.899842 59.34659 1 9.7242861 179.7773042 2018-02-01 02:00:00 night
2018 01 31 18 009 4.899842 59.34659 1 8.1432931 -174.1726553 2018-02-01 03:00:00 night

Hourly analysis

Use average hourly data directly from NORA3.

Show the code
ggplot(data = wind_100m, aes(x = time, y = u_mag)) +
  geom_point() +
  # geom_smooth(span = 0.3, se = TRUE) +
  # Cut-in
  geom_hline(yintercept = 3.5, col = "#009E73", linetype = "dashed") + 
  geom_text(x = as_datetime("2018-01-01 04:00:00"),
            y = 4,
            label = "Cut-in",
            colour = "#009E73") +
  # Cut-off
  geom_hline(yintercept = 25, col = "#D55E00", linetype = "dashed") +
  geom_text(x = as_datetime("2018-01-01 04:00:00"),
            y = 25.5,
            label = "Cut-off",
            colour = "#D55E00") +
  # Format labels and title
  labs(x = "Time",
       y = "u [m/s]",
       title = "Hourly wind speed at 100 m height", 
       subtitle = "Coordinates (WGS84): Long = 4.899842, lat = 59.346588",
       caption = "Data: NORA3") +
  coord_cartesian(ylim = c(0,30)) +
  theme_bw()
Figure 5: Hourly wind speed (m/s)

Histogram

Show the code
# Plot histogram 
ggplot() +
  geom_histogram(data = wind_100m, aes(x = u_mag, y =..density..),
                 binwidth = 0.5, 
                 fill = "#D55E00", 
                 colour = "grey") +
  # Format labels and title
  labs(x = "u [m/s]",
       title = "Histogram of hourly wind speed at 100 m height", 
       subtitle = "Point coordinates (WGS84): Long = 4.899842, lat = 59.346588",
       caption = "Data: NORA3") +
  coord_cartesian(xlim = c(0, 65)) +
  theme_bw() 
Figure 6: Wind speed histogram

Fit a Weibull distribution

Show the code
# Fit weibul distribution
fit <- fitdistrplus::fitdist(wind_100m$u_mag,"weibull")

# Put parameter in a table (inset element)
weibull_param <- tibble(param = rownames(as.data.frame(fit$estimate)),
                        estimate = fit$estimate,
                        std_Error = fit$sd)

# Generate values for addind the density curve
fit_values <- rweibull(1000000, shape = fit$estimate[1], scale = fit$estimate[2]) |> 
  as_tibble()

# Plot histogram together with the weibull distribution 
ggplot() +
  geom_histogram(data = wind_100m, aes(x = u_mag, y =..density..),
                 binwidth = 0.5, 
                 fill = "#D55E00", 
                 colour = "grey") +
  # Addd Weibull
  geom_density(data = fit_values,
               aes(x = value, y = ..density..),
               colour = "#0072B2",
               linewidth = 1) +
  # Format labels and title
  labs(x = "u [m/s]",
       title = "Weibull distribution of hourly wind speed at 100 m height", 
       subtitle = "Point coordinates (WGS84): Long = 4.899842, lat = 59.346588",
       caption = "Data: NORA3") +
  coord_cartesian(xlim = c(0, 65)) +
  theme_bw() +
  # Add inset table
  inset_element(gt(weibull_param), 0.60, 0.75, 0.95, 0.95)
Figure 7: Weibull distribution

Day-night variation in the wind speed distribution

Show the code
# Separate by day-night and fit a weibull for each group 
fit_weibull <- function(df) { fitdistrplus::fitdist(df$u_mag,"weibull") }

group_names <- wind_100m |> 
  group_by(day_night) |>
  group_keys() |>
  pull(1)

fit <- wind_100m |> 
  group_split(day_night) |> 
  set_names(group_names) |> 
  map(.f = fit_weibull)

# Put parameter in a table (inset element)
weibull_param <- tibble(param = c("shape", "scale"),
                        group = "day",
                        estimate = fit$day[[1]],
                        std_error = fit$day[[3]]
  ) |> 
  bind_rows(
    tibble(param = c("shape", "scale"),
           group = "night",
           estimate = fit$night[[1]],
           std_error = fit$night[[3]])
  )


# Generate values for adding the weibull curve
fit_values <- tibble(
  day = rweibull(1000000, shape = fit$day[[1]][1], scale = fit$day[[1]][2]),
  night = rweibull(1000000, shape = fit$night[[1]][1], scale = fit$night[[1]][2])
) |> 
  pivot_longer(cols = everything(),
               names_to = "day_night",
               values_to = "value"
               )

# Plot histograms
ggplot() +
  geom_histogram(data = wind_100m, aes(x = u_mag, y =..density..),
                 binwidth = 0.5, 
                 fill = "#D55E00", 
                 colour = "grey") +
  # Addd Weibull
  geom_density(data = fit_values,
               aes(x = value, y = ..density..),
               colour = "#0072B2",
               linewidth = 1) +
  # Format labels and title
  labs(x = "u [m/s]",
       title = "Weibull distribution of hourly wind speed at 100 m height", 
       subtitle = "Point coordinates (WGS84): Long = 4.899842, lat = 59.346588",
       caption = "Data: NORA3") +
  coord_cartesian(xlim = c(0, 65)) +
  facet_grid(~day_night) +
  theme_bw() +
  # Add inset table
  inset_element(gt(filter(weibull_param, group == "day") |> select(-group)),
                0.25, 0.8, 0.45, 0.95) +
  inset_element(gt(filter(weibull_param, group == "night") |> select(-group)),
                0.75, 0.8, 0.95, 0.95)
Figure 8: Histogram and weibull parameters of wind speed

Time bellow/above thresholds:

Show the code
# Time in production 
wind_100m <- wind_100m |> 
  select(time, u_mag, u_dir) |> 
  mutate(production = case_when(
    u_mag < 3.5 ~ FALSE,
    u_mag >  25 ~ FALSE,
    .default = TRUE)
    ) 

wind_100m |> 
  summarise(
    total_hours = n(),
    total_prod = sum(production),
    perc = 100 * mean(production)
  ) |> 
  gt() |> 
  fmt_number(columns = perc,
             decimals = 1)
Table 1: Percentage of time that a wind turbine is producing energy based on average hourly wind speed data (cut-in = 3.5 m/s, and cut-off = 25 m/s)
total_hours total_prod perc
744 688 92.5

Downscale to 10-min resolution

Create simulated data (n = 1000) based on NORA3 hourly data. Simulated data follow a Weibull distribution with the same global shape of day/night but mean values of the hourly data.

Show the code
set.seed(36)
n_sim = 1000

# Initial data frame  
sim_wind_100m <- tibble(time = seq(ymd_hm("2018-01-01 04:00"),
                                   ymd_hm("2018-02-01 03:50"), 
                                   by = "10 mins")) |> 
  # Add mean values and simulate weibull 
  left_join(wind_100m) |> 
  dplyr::select(time, u_mag) |> 
  fill(u_mag) |> 
  # Add columns indicatind day/night time
  mutate(day_night = ifelse(is_daytime(time, tz = "Europe/Oslo"), "day", "night")) 

# Simulations day
wd_shape <- weibull_param |> filter(group == "day", param == "shape") |> pull(estimate)
sim_wind_100m_day <- filter(sim_wind_100m, day_night == "day")
sim_day <- matrix(NA, length(sim_wind_100m_day$u_mag), n_sim)
u_mag <- sim_wind_100m_day$u_mag

for(i in 1:n_sim) {
  for(j in seq_along(u_mag)) {
    sim_day[j, i] = rweibull(1, shape = wd_shape, scale = u_mag[j] / gamma(1+(1/wd_shape)))
  }
}

sim_day <- sim_day |> 
  as_tibble() |> 
  rename_with(~ paste0("sim_", .)) |> 
  rename_with(~ gsub("V", "", .x, fixed = TRUE))

sim_wind_100m_day <- sim_wind_100m_day |> 
  bind_cols(sim_day)

# Simulations night
wd_shape <- weibull_param |> filter(group == "night", param == "shape") |> pull(estimate)
sim_wind_100m_night <- filter(sim_wind_100m, day_night == "night")
sim_night <- matrix(NA, length(sim_wind_100m_night$u_mag), n_sim)
u_mag <- sim_wind_100m_night$u_mag
for(i in 1:n_sim) {
  for(j in seq_along(u_mag)) {
    sim_night[j, i] = rweibull(1, shape = wd_shape, scale = u_mag[j] / gamma(1+(1/wd_shape)))
  }
}

sim_night <- sim_night |> 
  as_tibble() |> 
  rename_with(~ paste0("sim_", .)) |> 
  rename_with(~ gsub("V", "", .x, fixed = TRUE))

sim_wind_100m_night <- sim_wind_100m_night |> 
  bind_cols(sim_night)

# Merge data
sim_wind_100m <- sim_wind_100m_day |> 
  bind_rows(sim_wind_100m_night) |> 
  arrange(time)
Show the code
# Plot example of 4 simulations
sim_wind_100m |> 
  select("sim_1", "sim_2", "sim_3", "sim_4") |> 
  pivot_longer(cols = starts_with("sim"))  |> 
  ggplot(aes(x = value)) +
  geom_histogram(aes(y =..density..),
                 bins = 50,
                 fill = "#D55E00",
                 colour = "grey") +
  geom_density() +
  labs(title = "Wind distribution in simulated data",
       subtitle = "Example of the first four simulations",
       x = "u [m/s]") +
  facet_wrap(~ name, ncol = 2) +
  theme_bw() 
Figure 9
Show the code
# Exampla first four simulations
sim_wind_100m |> 
  select("time","u_mag", "sim_1", "sim_2", "sim_3", "sim_4") |> 
  pivot_longer(cols = starts_with("sim")) |> 
  ggplot() +
  geom_point(aes(x = time, y = value),
             colour = "#0072B2",
             size = 0.6,
             alpha = 0.5) + 
  # Hourly points
  geom_line(data = wind_100m, aes(x = time, y = u_mag), col = "red") +
  # Cut-in
  geom_hline(yintercept = 3.5, col = "#009E73", linetype = "dashed") +
  # Cut-off
  geom_hline(yintercept = 25, col = "#D55E00", linetype = "dashed") +
  # Format labels and title
  labs(x = "Time",
       y = "u [m/s]",
       title = "Simulated wind speed at 100 m height (10 min resolution)", 
       subtitle = "Exampla of the first four simulations",
       caption = "Red line indicates hourly mean value from NORA3") +
  facet_wrap(~ name, ncol = 2) +
  theme_bw()
Figure 10: Hourly wind speed (m/s) of simntetic data
Show the code
sim_wind_100m |> 
  pivot_longer(cols = starts_with("sim")) |> 
  ggplot() +
  # geom_point(aes(x = time, y = value),
  #            colour = "#0072B2",
  #            size = 0.01,
  #            alpha = 0.05) + 
  geom_hex(aes(x = time, y = value), bins = 500) +
  viridis:: scale_fill_viridis(trans = "log10") +
  # Hourly points
  geom_line(data = wind_100m,
            aes(x = time, y = u_mag), 
            col = "red",
            alpha = 0.5) +
  # Cut-in
  geom_hline(yintercept = 3.5, col = "#009E73", linetype = "dashed") +
  # Cut-off
  geom_hline(yintercept = 25, col = "#D55E00", linetype = "dashed") +
  # Format labels and title
  labs(x = "Time",
       y = "u [m/s]",
       title = "Simulated wind speed at 100 m height (10 min resolution)", 
       subtitle = "All simulations",
       caption = "Red line indicates hourly mean value from NORA3") +
  theme_bw()

Hourly average vs. NORA3

Show the code
sim_wind_100m |>  
  mutate(date_hour = format(time, "%Y-%m-%d %H")) |> 
  relocate(date_hour, .after = time) |> 
  pivot_longer(cols = starts_with("sim")) |> 
  group_by(date_hour) |> 
  summarise(
    n = n(),
    time = first(time),
    u_mag = first(u_mag),
    sim_mean = mean(value)
    ) |> 
  ungroup() |> 
  ggplot() +
  geom_point(aes(x = u_mag, y = sim_mean), alpha = 0.5) +
  labs(title = "Hourly wind speed [m/s]",
       y = "Simulated data",
       x = "NORA3 data") +
  geom_abline(intercept = 0, slope = 1, col = "red") +
  coord_equal() + 
  theme_bw()
Figure 11: Relationship between the hourly wind speed calculated in the simulated data and NORA3 data values
Show the code
sim_wind_100m_hour <- sim_wind_100m |>  
  mutate(date_hour = format(time, "%Y-%m-%d %H")) |> 
  relocate(date_hour, .after = time) |> 
  pivot_longer(cols = starts_with("sim")) |> 
  tidytable::group_by(date_hour,name) |> 
  tidytable::summarise(
    n = n(),
    time = first(time),
    u_mag = first(u_mag),
    sim_mean = mean(value)
  ) |> 
  tidytable::ungroup() 


sim_wind_100m_hour |> 
  ggplot() +
  geom_hex(aes(x = u_mag, y = sim_mean), bins = 120) +
  viridis::scale_fill_viridis(trans = "log10") +
  labs(title = "Hourly wind speed [m/s]",
       y = "Simulated data",
       x = "NORA3 data") +
  geom_abline(intercept = 0, slope = 1, col = "red") +
  coord_equal() +
  theme_bw()

Show the code
# Time in production 
sim_wind_100m_hour <- sim_wind_100m_hour |> 
  mutate(production = case_when(
    sim_mean < 3.5 ~ FALSE,
    sim_mean >  25 ~ FALSE,
    .default = TRUE)
    ) 

# Time in production
sim_wind_100m_hour_prod <- sim_wind_100m_hour |> 
  group_by(name) |> 
  summarise(
    total_hours = n(),
    total_prod = sum(production),
    perc = 100 * mean(production)
  ) |> 
  ungroup()


# Plot percentage
sim_wind_100m_hour_prod |> 
  ggplot(aes(x = perc)) +
  geom_histogram(aes(y =..density..),
                 bins = 40,
                 fill = "#D55E00",
                 colour = "grey") +
  geom_density() +
  labs(x = "Perc. [%]",
       title = "Histogram of the percentage the time in production",
       subtitle = "Based on hourly means of simulated data",
       caption = "Wind speed between cut-in = 3.5 m/s and cut-off = 25 m/s") +
  theme_bw() 

Percentage of time that a wind generator is producing energy in the 10 minute simulated data:

Show the code
prod_10min <- sim_wind_100m |> 
  pivot_longer(cols = starts_with("sim")) |> 
  group_by(name) |>
  mutate(production = case_when(
    value < 3.5 ~ FALSE,
    value >  25 ~ FALSE,
    .default = TRUE)
    ) |> 
  summarise(
    total_hours = n() * 10/60,
    total_prod = sum(production) * 10/60,
    perc = 100 * mean(production)
    ) |> 
  ungroup()


# Plot
prod_10min |> 
  ggplot(aes(x = perc)) +
  geom_histogram(aes(y =..density..),
                 bins = 40,
                 fill = "#D55E00",
                 colour = "grey") +
  geom_density() +
  labs(x = "Perc. [%]",
       title = "Histogram of the percentage of hours in production",
       subtitle = "Wind speed between cut-in = 3.5 m/s and cut-off = 25 m/s") +
  theme_bw() 
Figure 12: Percentage of time that a wind generator is producing energy based on simulated data (cut-in = 3 m/s and cut-off = 25 m/s)
Show the code
prod_10min |> 
  select("total_hours", "total_prod", "perc") |> 
  purrr::map_dfr(.f = summary, .id = "value") |> 
  mutate_at(vars(-value), as.numeric) |> 
  gt() |> 
  fmt_number(columns = vars(-value),
             rows = c(1,2),
             decimals = 0) |> 
  fmt_number(columns = vars(-value),
             rows = 3,
             decimals = 1)
Table 2: Percentage of time that a wind generator is producing energy based on simulated data (cut-in = 3 m/s and cut-off = 25 m/s)
value Min. 1st Qu. Median Mean 3rd Qu. Max.
total_hours 744 744 744 744 744 744
total_prod 555 565 568 568 571 580
perc 74.6 75.9 76.3 76.3 76.7 77.9

Analyse variability in the data.

Solbrekke, Ida Marie, and Asgeir Sorteberg. 2022. “NORA3-WP: A High-Resolution Offshore Wind Power Dataset for the Baltic, North, Norwegian, and Barents Seas.” Scientific Data 9 (1): 362. https://doi.org/10.1038/s41597-022-01451-x.